icdcs 2017: program at a glanceicdcs2017.gatech.edu/.../icdcs2017-program-posting-v17.pdfvision/blue...

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ICDCS 2017

IEEE 37th International Conference on Distributed Computing Systems

June 5-8, 2017 Atlanta, USA

Editors

Dr. Kisung Lee Louisiana State University, USA

Dr. Ling Liu Georgia Institute of Technology, USA

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Table of Contents

Conference Hotel Floor Plan .................................................................................................. 3

Message from the General Chairs and PC Chair .................................................................... 4

Organizing Committee ............................................................................................................ 6

Program Committee ................................................................................................................ 7

Keynotes ............................................................................................................................... 16

Program at a Glance ............................................................................................................. 19

Day 1 – Monday, June 5, 2017 .............................................................................................. 21

Day 2 – Tuesday, June 6, 2017 ............................................................................................. 28

Day 3 – Wednesday, June 7, 2017 ........................................................................................ 39

Day 4 – Thursday, June 8, 2017 ............................................................................................ 49

Research Track Paper Abstracts ......................................................................................... 55

Industry and Experimentation Track Paper Abstracts ........................................................ 73

Applications and Experiences Track Paper Abstracts ........................................................ 76

Vision/Blue Sky Thinking Track Paper Abstracts ................................................................ 88

Short Paper Abstracts .......................................................................................................... 95

Demonstration Track Paper Abstracts .............................................................................. 108

Poster Track Paper Abstracts ............................................................................................ 112

Tutorial Abstracts ............................................................................................................... 119

ADSN 2017 Workshop Abstracts ........................................................................................ 121

BGP 2017 Workshop Abstracts .......................................................................................... 122

CCN-CPS 2017 Workshop Abstracts .................................................................................. 124

HotPOST 2017 Workshop Abstracts .................................................................................. 127

IoTCA 2017 Workshop Abstracts ....................................................................................... 129

JCC 2017 Workshop Abstracts ........................................................................................... 131

PED 2017 Workshop Abstracts ........................................................................................... 134

PSBD 2017 Workshop Abstracts ........................................................................................ 135

WoSC 2017 Workshop Abstracts ........................................................................................ 136

NSF-JST 2017 Workshop Abstracts ................................................................................... 137

Local Information ............................................................................................................... 138

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Conference Hotel Floor Plan

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Message from the General Chairs and PC Chair

WewouldliketowelcomeyoutoICDCS2017,the37thIEEEInternationalConferenceonDistributedComputingSystems,beingheldfromJune5toJune8,2017,inAtlanta,Georgia,USA.TheconferenceisheldintheJ.W.MarriottHotel,convenientlylocatedinBuckhead,Atlanta.

Asapremierforumforthepresentationofresearchresultsonabroaderspectrumofdistributedcomputingsystems,the2017ICDCScontinuesthetraditionwithsimilarorganizationalstructure,andintroducessomeinnovations.

Theresearchtrackshavereceivedatotalof531originalsubmissionstotheelevenresearchtracks.Eachpaperwasreviewedbyatleastthreeindependentreviewers,anddiscussedduringtheonlinePCmeetingheldbytrackchairs.Furthermore,thetoprankedpapersfromeachtrackwerefurtherdiscussedduringthefacetofacetrackchairsmeetingheldatGeorgiaInstituteofTechnologyinAtlanta.Atotalof89papershavebeenacceptedbytheresearchtrackswiththeacceptanceratioof16.8%.Wewouldliketoexpressourheartfeltthankstoallofthetrackchairs:MichaelKozuchandAnshulGandhi(CloudandDataCenters),SrinivasAluru(BigDataSystemsandAnalytics),FredDouglis(OSandMiddleware),DougBlough(DistributedFaultTolerantComputing),ElisaBertino(Security,PrivacyandTrust),KarthikSundaresan(WirelessandMobileComputing),KuiRen(DistributedAlgorithms),CanturkIsci(DistributedGreenComputing),ThiemoVoigt(InternetofThingsandCyberPhysicalSystems),WeisongShiandTaoZhang(EdgeandFogComputing),MunindarSingh(SocialandCrowdComputing).

Inaddition,ICDCS2017technicalprogramalsoconsistsofindustrialandexperimentationsessions,applicationsandexperiencessessions,shortpaperssessions,postersanddemossessions,tutorials,andco-locatedworkshops.AnothertwohighlightsofICDCS2017are:(1)thethreekeynotesbydistinguishedspeakers:Dr.C.MohanfromIBMAlmaden,Prof.Dr.TamerOzsufromWaterlooUniversity,andDr.KennethCalvertfromNationalScienceFoundation,and(2)theBlueSkyeIdeasandVisionsessionsfeaturingthought-provokingandforwardlookingblueskyideasandvisionsonbroaderspectrumsoffuturedistributedcomputingsystems.Inaddition,the2017ICDCSmainconferenceisprecededbyeightco-locatedworkshops,describedintheworkshopchairs’message.Theseworkshopsprovideanopportunityforsmallgroupsoflike-mindedresearcherstodiscussareasofinterestandnewideas,andwearepleasedtoseeadiverseecosystemofworkshopsatICDCS.

Thesuccessof2017ICDCSreliedonthecontributionsofagreatteamofvolunteers,includingtheprogramcommitteemembersfromalltracks.Wewouldliketothankallofthemfortheirtime,highqualityservice,andtirelessefforts.Wewouldliketoexpressourspecialappreciationtotheindustryandexperimentationchairs(ScottKlasky,AameekSingh),theblueskyideasandvisiontrackchairs(ManfredHauswirth,ManishParashar),theapplicationsandexperienceschairs(SongqingChen,SangeethaSeshadri),theshortpaperchairs(LaksmishRamaswamy,JianweiYin),andthedemoandposterstrackchairs(GeraldLofstead,BalajiPalanisamy),theworkshopprogramchairs(JoaoE.Ferreira,TeruoHigashino),tutorialchairs(JamesCaverlee,VaidySunderam),panelchairs(DimitriosGeorgakopoulos,YiPan,YinglongXia),publicationchair(KisungLee),publicitychair(AibekMusaev,EmreYigitoglu),localarrangementchairs(AibekMusaev,JoshuaKimball,WeiZhou),andfinance/registrationchair(CarrieStein).

Finally,onbehalfofthe2017ICDCSorganizationandprogramcommittee,wewouldliketoexpressoursinceregratitudetoeveryonewhohascontributedtotheconference,especiallytheauthorsandtheparticipants.WewouldliketothankXiaodongZhang(ICDCSSteeringCommitteeChair)andCheng-ZhongXu(chairofIEEEComputerSocietyTechnicalCommitteeonDistributedProcessing)fortheirsupport,adviceandtrustingustoorganizetheconference.Theyalsospearheadedthesuccessfulefforttoobtainanddisbursefundingtosponsorthestudenttravelawards.Inaddition,wewouldliketoacknowledgethesponsorshipsupportbytheNationalScienceFoundation(NSF/USA)andJapanScienceandTechnologyAgency(JST/Japan)forthe1stUS-JapanWorkshoponEnablingGlobalCollaborationsinBigDataResearch.

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WewishyouanenjoyableandproductiveconferenceandapleasantstayinAtlanta.

ProgramChair:

LingLiu,GeorgiaInstituteofTechnology,USA

GeneralChairs:

CaltonPu,GeorgiaInstituteofTechnology,USA

MasaruKitsuregawa,NIIandU.Tokyo,Japan

KarlAberer,EPFL,Switzerland

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Organizing Committee

General Co-Chairs Calton Pu, Georgia Institute of Technology, USA Masaru Kitsuregawa, University of Tokyo, Japan Karl Aberer, EPFL, Switzerland

Program Chair

Ling Liu, Georgia Institute of Technology, USA Workshops Program Chairs

Joao E. Ferreira, University of Sao Paulo, Brazil Teruo Higashino, University of Osaka, Japan

Tutorial Chairs

James Caverlee, Texas A&M University, USA Vaidy Sunderam, Emory University, USA

Panel Chairs

Dimitrios Georgakopoulos, Swinburne University, Australia Yi Pan, Georgia State University, USA Yinglong Xia, Huawei Research America, USA

Short Papers Track Chairs

Laksmish Ramaswamy, UGA, USA Jianwei Yin, Zhejiang University, PR China

Demonstration and Posters Track Chairs

Gerald F Lofstead, Sandia National Laboratories, USA Balaji Palanisamy, University of Pittsburg, USA

Publication Chair

Kisung Lee, Louisiana State University, USA Workshops Publication Chair

Aibek Musaev, University of Alabama, USA Publicity Chair, Local Arrangement Chair, Webmaster

Aibek Musaev, University of Alabama, USA Publicity Co-Chair

Emre Yigitoglu, Georgia Institute of Technology, USA Steering Committee Chair

Xiaodong Zhang, Ohio State University, USA Finance Manager (non-volunteer, reporting to General Chairs)

Carrie Stein, Ohio State University, USA

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Program Committee

Program Chair

Ling Liu, Georgia Institute of Technology, USA Program Vice Chairs and TPC Members (Research Tracks) § Cloud Computing and Data Centers

Co-Chair: Michael A. Kozuch, Intel, USA Co-Chair: Anshul Gandhi, Stony Brook University, USA § George Amvrosiadis, Carnegie Mellon University § Ganesh Ananthanarayanan, Microsoft Research § Danilo Ardagna, Politecnico di Milano § Alvin AuYoung, Uber § Bharath Balasubramanian, ATT Labs Research § Sonia Ben Mokhtar, LIRIS CNRS § Sara Bouchenak, INSA, Lyon § Ali R. Butt, Virginia Tech § Irina Calciu, VMware Research Group § Maria Carla Calzarossa, Universita di Pavia § Giovanni Toffetti Carughi, ZHAW § Giuliano Casale, Imperial College London § Claris Castillo, RENCI@UNC Chapel Hill § Lydia Y. Chen, IBM Research Zurich Lab § Keke Chen, Wright State University § Byung-Gon Chun, Seoul National University § Erik Elmroth, Umeå University and Elastisys § Renato Figueiredo, University of Florida § Sahan Gamage, VMware § Christos Gkantsidis, Microsoft Research § Chris Gniady, University of Arizona § Kartik Gopalan, Binghamton University § Daniel Grosu, Wayne State University § Xiaohui Gu, North Carolina State University § Haryadi S. Gunawi, University of Chicago § Yu Hua, Huazhong University of Science and Technology § Alexandru Iosup, Delft University of Technology § Evangelia Kalyvianaki, City, University of London § Eric Keller, University of Colorado, Boulder § Steve Ko, SUNY Buffalo § Donald Kossmann, Microsoft Research Redmond & ETH Zurich § Diwakar Krishnamurthy, University of Calgary § Purushottam Kulkarni, Indian Institute of Technology, Bombay § John Lange, University of Pittsburgh § Marin Litoiu, York University § Zhenhua Liu, Stony Brook University § Fangming Liu, Huazhong University of Science and Technology § Amiya Maji, Purdue University § Lena Mashayekhy, University of Delaware § Thu Nguyen, Rutgers University § Balaji Palanisamy, University of Pittsburgh

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§ Donald E. Porter, The University of North Carolina at Chapel Hill § Chen Qian, University of California Santa Cruz § Ioan Raicu, Illinois Institute of Technology § Shriram Rajagopalan, IBM Watson Research § Shaolei Ren, University of California, Riverside § Henry Robinson, Cloudera § Stefan Schmid, Aalborg University & TU Berlin § Haiying Shen, University of Virginia § Evgenia Smirni, College of William and Mary § Yuzhe Tang, Syracuse University § Devesh Tiwari, ORNL § Bhuvan Urgaonkar, Pennsylvania State University § Xiaorui Wang, The Ohio State University § Anduo Wang, Temple University § Jianguo Yao, Shanghai Jiao Tong University § Ming Zhao, Arizona State University § Xiaobo Zhou, University of Colorado, Colorado Springs § Xiaoyun Zhu, Futurewei Technologies, Inc.

§ Distributed Big Data Systems & Analytics Chair: Srinivas Aluru, Georgia Institute of Technology, USA § Gagan Agrawal, Ohio State University § Rajesh Bordawekar, IBM T. J. Watson Research Center § Polo Chau, Georgia Institute of Technology § Alin Dobra, University of Florida § Vijay Gadepally, Massachusetts Institute of Technology § Bugra Gedik, Bilkent University, Turkey § Tony Xiaohua Hu, Drexel University § Hans-Arno Jacobsen, University of Toronto § George Karypis, University of Minnesota § Tevfik Kosar, State University of New York at Buffalo § Zhenhui (Jessie) Li, Pennsylvania State University § Ron Oldfield, Sandia National Laboratories § Srinivasan Parthasarathy, Ohio State University § Peter Pietzuch, Imperial College § Vijay Raghavan, Louisiana State University § Yogesh Simmhan, Indian Institute of Science § Nesime Tatbul, Intel Labs and Massachusetts Institute of Technology § Ying Xie, Kennesaw State University § Jianfeng Zhan, Chinese Academy of Sciences § Justin Z. Zhan, University of Nevada Las Vegas

§ Distributed Operating Systems & Middleware Chair: Fred Douglis, Dell EMC, USA § Paolo Bellavista, University of Bologna, Italy § Surendar Chandra, Datrium, USA § Yuan Chen, Hewlett Packard Labs, USA § Geoffrey Fox, Indiana University, USA § Indranil Gupta, University of Illinois at Urbana-Champaign, USA § Yu Hua, Huazhong University of Science and Technology, China

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§ Hong Jiang, University of Texas at Arlington, USA § Vana Kalogeraki, Athens Institute of Economics and Business, Greece § Fabio Kon, University of São Paulo, Brazil § Xiaosong Ma, Qatar Computing Research Institute, Qatar § Rajesh Panta, AT&T Labs – Research, USA § Luís Rodrigues, IST, ULisboa, Portugal § Liuba Shrira, Brandeis University, USA § Francois Taiani, University of Rennes 1, IRISA / Inria, France § Maarten Van Steen, University of Twente, Netherlands § Chitra Venkatramani, Google, USA § Zhen Xiao, Peking Univ, China § Gala Yadgar, Technion, Israel § Ming Zhao, Arizona State University, USA

§ Distributed Algorithms and Theory Chair: Kui Ren, SUNY, Buffalo, USA § Jiannong Cao, Hong Kong Polytechnic University § Xiuzhen Cheng, The George Washington University § Ding-Zhu Du, University of Texas at Dallas § Michael Fang, University of Florida § Vincent Gramoli, University of Sydney § Xiaohua Jia, City University of Hong Kong § Murat Kantarcioglu, University of Texas at Dallas § Nei Kato, Tohoku University § Sastry Kompella, Naval Research Lab § Sam Kwong, City University of Hong Kong § Baochun Li, University of Toronto § Xiangyang Li, University of Science and Technology of China § Phone Lin, National Taiwan University § Benyuan Liu, University of Massachusetts Lowell § Jiangchuan Liu, Simon Fraser University § Xue Liu, McGill University § Tommaso Melodia, Northeastern University § Aziz Mohaisen, SUNY Buffalo § Guevara Noubir, Northeastern University § Francesco Pasquale, Sapienza Università di Roma § Sherman Shen, University of Waterloo § Paul Spirakis, RA.CTI and University of Patras § Anna Squicciarini, The Pennsylvania State University § Lu Su, SUNY Buffalo § My Thai, University of Florida § Peng-Jun Wan, Illinois Institiute of Technology § Jie Wu, Temple University § Nicholas Zhang, Huawei Technologies § Sheng Zhong, SUNY Buffalo

§ Distributed Fault Tolerance & Dependability Chair: Doug Blough, Georgia Institute of Technology, USA § Emmanuelle Anceaume, IRISA, France § Saurabh Bagchi, Purdue University, USA

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§ Antonio Casimiro, University of Lisbon, Portugal § Xavier Defago, Tokyo Institute of Technology, Japan § Felicita di Giandomenico, ISTI-CNR, Italy § Elias Duarte, Federal University of Parana, Brazil § Christof Fetzer, Technical University of Dresden, Germany § Roy Friedman, Technion, Israel § Patrick Lee, Chinese University of Hong Kong, China § Michael Lyu, Chinese University of Hong Kong, China § Cristina Nita-Rotaru, Northeastern University, USA § Fernando Pedone, University of Lugano, Switzerland § Sebastiano Peluso, Virginia Tech, USA § Ravi Prakash, University of Texas at Dallas, USA § Tatsuhiro Tsuchiya, Osaka University, Japan § Ymir Vigfusson, Emory University, USA

§ Distributed Green Computing & Energy Management Chair: Canturk Isci, IBM T.J. Watson, USA § Niklas Carlsson, Linköping University, Sweden § Ayse Coskun, Boston University § Yuanxiong (Richard) Guo, Oklahoma State University § David Irwin, University of Massachusetts Amherst § Prabhakar Kudva, IBM TJ Watson Research Center § Fangming Liu, Huazhong University of Science & Technology § Vijay Janapa Reddi, University of Texas at Austin § Suzanne McIntosh, Cloudera & NYU § Shaolei Ren, University of California, Riverside § Tajana Simunic Rosing, University of California, San Diego § Anand Sivasubramaniam, Pennsylvania State University § Christopher Stewart, Ohio State University § Devesh Tiwari, Oak Ridge National Laboratory § Di Wang, Microsoft Research, Redmond § Carole-Jean Wu, Arizona State University

§ Internet of Things & Cyber-Physical Systems Chair: Thiemo Voigt, SICS and Uppsala University, Sweden § Tarek Abdelzaher, University of Illinois at Urbana Champaign, USA § Carlo Boano, TU Graz, Austria § Qing Cao, University of Tennessee, USA § Octav Chipara, University of Iowa, USA § Wan Du, Nanyang Technological University, Singapore § Simon Duquennoy, Inria, France, and SICS, Sweden § Yuan He, Tsinghua University, China § Wen Hu, University of New South Wales, Australia § JeongGil Ko, Ajou University, South Korea § Linghe Kong, Shanghai Jiao Tong University, China § Mike Chieh-Jan Liang, Microsoft Research Asia § Margaret Loper, Georgia Institute of Technology, USA § Pedro Marron, University of Duisburg-Essen, Germany § Luca Mottola, Politecnico di Milano, Italy and SICS, Sweden § Edith Ngai, Uppsala University, Sweden

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§ Gian Pietro Picco, University of Trento, Italy § Mo Sha, SUNY Binghamton, USA § David Smith, Data61, CSIRO, Australia § Eduardo Tovar, Polytechnic Institute of Porto, Portugal § Chenshu Wu, Tsinghua University, China § I-Ling Yen, University of Texas at Dallas, USA § Haibo Zhang, University of Otago, New Zealand

§ Mobile and Wireless Network Computing Chair: Karthik Sundaresan, NEC Labs Princeton, USA § Ehsan Aryafar, Intel, USA § Zhipeng Cai, Georgia State Univ § Mehmet Can Vuran, Univ of Nebraska Lincoln § Guohong Cao, Penn State Univ § Aveek Dutta, Univ at Albany § Monisha Ghosh, Univ of Chicago § Tian He, Univ of Minnesota § Ravi Kokku, IBM research § Dimitrios Koutsonikolas, Univ of Buffalo § Sandeep Kulkarni, Michigan State Univ § Mo Li, NTU § Shiwen Mao, Auburn Univ § Joerg Ott, Aalto University § Neal Patwari, Univ of Utah § Feng Qian, Indiana University § Sampath Rangarajan, NEC Labs America § Kay Romer, ETH Zurich § Theodoros Salonidis, IBM research § Thrasyvoulos Spyropoulos, Eurocom § Lu Su, Univ of Buffalo § Nalini Venkatasubramanian, Univ of California, Irvine § Xinbing Wang, Shanghai Jiao Tong Univ § Jie Wu, Temple Univ § Jie Xiong, Singapore Management University § Jongwon Yoon, Hanyang university, Korea § Moustafa A. Youssef, Egypt-Japan University of Science and Technology (E-JUST)

§ Edge and Fog Computing Co-Chair: Weisong Shi, Wayne State University, USA Co-Chair: Tao Zhang, Cisco, USA § Gary Chan, The Hong Kong University of Science and Technology § Dilma Da Silva, Texas A&M University § Schahram Dustdar, TU Wien § Sangtae Ha, University of Colorado at Boulder § Robert Hall, AT&T Labs Research § Bhaskar Krishnamachari, USC § Tian Lan, GWU § Martin Lehofer, Siemens VAI Metals Technologies GmbH § Qun Li, College of William and Mary § Michael Rabinovich, Case Western Reserve University

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§ Eve Schooler, Intel Research § Andrew Weinert, MIT § Fan Ye, Stony Brook University § Junshan Zhang, Arizona State University § Yanyong Zhang, Rutgers University

§ Security, Privacy, Trust in Distributed Systems Chair: Elisa Bertino, Purdue University, USA § Ehab Al-Shaer, UNC Charlotte § Salman A Baset, IBM Research § Nilanjan Banerjee, UMBC § Barbara Carminati, University of Insubria, Italy § Aniello Castiglione, University of Salerno, Italy § Dario Catalano, University of Catania, Italy § Yan Chen, Northwestern University § Lucas Davi, University of Duisburg-Essen, Germany § Robert Deng, Singapore Management University § Elena Ferrari, University of Insubria, Italy § Aurelien Francillon, Eurecom § Michael Franz, University of California at Irvine § Gabriel Ghinita, UMass at Boston § Guofei Gu, Texas A&M § Xinyi Huang, Fujian Normal University § Aniket Kate, Purdue University § Florian Kerschbaum, SAP § Taesoo Kim, Georgia Institute of Technology § Zhenkai Liang, National University of Singapore § Dan Lin, Missouri University of Science and Technology § Alex Liu, Michigan State University § Surya Nepal, CSIRO, Australia § Kui Ren, SUNY Buffalo § Seung-Yun Seo, Korea University § Anna Squicciarini, Pennsylvania State University § Cong Wang, Hong Kong City University § Danfeng Yao, Virginia Tech § Xun Yi, RMIT, Australia § Ting Yu, Qatar Computing Research Institute

§ Social Networks and Crowdsourcing Chair: Munindar P. Singh, NCSU, USA § Alessandro Bozzon, Delft University of Technology, Netherlands § James Caverlee, Texas A&M University, USA § Peng Cui, Tsinghua University, China § Raghu Ganti, IBM T J Watson Research Center, USA § Chung-Wei Hang, IBM, USA § Catholijn Jonker, Delft University of Technology, Netherlands § Anup Kalia, IBM T. J. Watson Research Center, USA § Vana Kalogeraki, Athens University of Economics and Business, Greece § Sharad Mehrotra, University of California, Irvine, USA § Pradeep Kumar Murukannaiah, Rochester Institute of Technology, USA

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§ Mirco Musolesi, University College London, UK § Nishanth Sastry, King’s College London, UK § Murat Sensoy, Ozyegin University, Turkey § Erez Shmueli, Tel-Aviv University, Turkey § Mudhakar Srivatsa, IBM T.J. Watson Research Center, USA § Guangchao Yuan, Microsoft, USA § Zhe Zhang, IBM Watson, USA

Special Tracks TPC Chairs and TPC Members

§ Industry and Experimentation

Co-Chair: Scott Klasky, Oak Ridge National Lab. USA Co-Chair: Aameek Singh, IBM T.J. Watson § Dorian Arnold, UNM, USA § Rong Chang, IBM, USA § Carlos Costa, IBM, USA § Alfredo Cuzzocrea, University of Trieste, Italy § Raghu Ganti, IBM, USA § Garth Gibson, CMU, USA § Tahsin Kurc, SBU, USA § Qing Liu, NJIT, USA § Xuanzhe Liu, Peking University, China § Heiko Ludwig, IBM, USA § Carlos Maltzahn, UCSC, USA § Nagapramod Mandagere, IBM, USA § Manish Parashar, Rutgers, USA § Yang Song, IBM, USA § Matthew Wolf, Oak Ridge National Lab., USA § Pengcheng Xiong, Hortonworks, USA § Pingpeng Yuan, Huazhong University of Science and Technology, China § Fan Zhang, IBM, USA § Yiming Zhang, National University of Defense Technology, China § Xiaomin Zhu, National University of Defense University, China

§ Applications and Experiences

Co-Chair: Songqing Chen, George Mason University, USA Co-Chair: Sangeetha Seshadri, IBM Almaden, USA § Kun Bai, IBM TJ Watson Research Center, USA § Bhuvan Bamba, Oracle, USA § Anu Bourgeois, Georgia State University, USA § Umesh Deshpande, IBM, USA § Xiaoning Ding, New Jersey Institute of Technology, USA § Vijay Gopalakrishnan, AT&T Labs – Research, USA § Yang Guo, NIST, USA § Vana Kalogeraki, Athens University of Economics and Business, Greece § Kisung Lee, Louisiana State University, USA § Qi Li, Tsinghua University, China § Fei Li, George Mason University, USA § Baochun Li, University of Toronto, Canada § Yao Liu, SUNY Binghamton, USA

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§ Aziz Mohaisen, SUNY Buffalo, USA § Shannon Quinn, University of Georgia, USA § Lukas Rupprecht, Imperial College London, UK § Aki-Hiro Sato, Kyoto University, Japan § Viswanathan Swaminathan, Adobe, USA § Masashi Toyoda, Univ Tokyo, Japan § Qingyang Wang, Louisiana State University, USA § Ting Wang, Lehigh University, USA § Li Xiao, Michigan State University, USA § Weijun Xiao, Virginia Commonwealth University, USA § Jianwei Yin, Zhejiang University, China § Gong Zhang, Oracle Corporation, USA § Rui Zhang, Institute of Information Engineering, Chinese Academy of Sciences, China § Yanqing Zhang, Georgia State University, USA § Zhao Zhang, University of Illinois at Chicago, USA § Xuan Zhou, Renmin University of China, China

§ Vision / Blue Sky Thinking Co-Chair: Manfred Hauswirth, TU Berlin, Germany Co-Chair: Manish Parashar, Rutgers University, USA § Ozalp Babaoglu, University of Bologna, Italy § Umesh Bellur, IIT Bombay, India § Jesus Carretero, Universidad Carlos III de Madrid, Spain § Frederic Desprez, INRIA, France § Dieter Kranzlmüller, Ludwig-Maximilians-Universitaet Muenchen, Germany § Christine Morin, INRIA, France § Thu Nguyen, Rutgers University, USA § Vladimir Vlassov, Royal Institute of Technology (KTH), Sweden

Short Papers Track

Co-Chair: Laksmish Ramaswamy, UGA, USA Co-Chair: Jianwei Yin, Zhejiang University, PR China § Amir Abdolrashidi, The University of Georgia, USA § Cao Bin, College of Computer Science and Technology, Zhejiang University of Technology, China § Vinay Boddula, University of Georgia, USA § Arash Fard, Hewlett Packard Enterprise, USA § Rong Ge, Clemson University, USA § Takahiro Hara, Graduate School of Information Science and Technology, Osaka University, Japan § Kyu Lee, University of Georgia, USA § Wenjia Li, New York Institute of Technology, USA § Xinjian Lu, East China University of Science and Technology, China § Shuai Ma, Beihang University, China § Rohit Mullangi, University of Georgia, USA § Aibek Musaev, University of Alabama, USA § Surya Nepal, CSIRO, Australia § Balaji Palanisamy, University of Pittsburgh, USA § Shannon Quinn, University of Georgia, USA § Michael Sheng, The University of Adelaide, Australia § Varun Soundararajan, Google, USA § Anna Squicciarini, The Pennsylvania State University, USA

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§ Yueshen Xu, Xidian University, China § Yun Yang, Swinburne University of Technology, Australia § Emre Yigitoglu, Georgia Institute of Technology, USA

Demonstration and Posters Track

Co-Chair: Gerald F Lofstead, Sandia Nat. Lab., USA Co-Chair: Balaji Palanisamy, University of Pittsburg, USA § Nathalie Baracaldo, IBM Research, USA § Suren Byna, Lawrence Berkeley National Laboratory, USA § Toni Collis, EPCC, UK § Liting Hu, Florida International University, USA § Suzanne McIntosh, NYU Courant Institute, and Cloudera Inc., USA § Devesh Tiwari, ORNL, USA § Ming Zhao, Arizona State University, USA

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Keynotes

Keynote 1: C. Mohan (IBM Research)

NewErainDistributedComputingwithBlockchainsandDatabases

Aneweraisemergingintheworldofdistributedcomputingwiththegrowingpopularityofblockchains(shared,replicatedanddistributedledgers)andtheassociateddatabasesasawayofintegratinginter-organizationalwork.Originally,theconceptofadistributedledgerwasinventedastheunderlyingtechnologyofthecryptocurrencyBitcoin.Buttheadoptionandfurtheradaptationofitforuseinthecommercialorpermissionedenvironmentsiswhatisofutmostinteresttomeandhencewillbethefocusofthiskeynote.ComputercompanieslikeIBMandMicrosoft,andmanykeyplayersindifferentverticalindustrysegmentshaverecognizedtheapplicabilityofblockchainsinenvironmentsotherthancryptocurrencies.IBMdidsomepioneeringworkbyarchitectingandimplementingFabric,andthenopensourcingit.NowFabricisbeingenhancedviatheHyperledgerConsortiumaspartofTheLinuxFoundation.AfewoftheothereffortsincludeEnterpriseEthereum,R3CordaandBigchainDB.

Whilethereisnostandardintheblockchainspacecurrently,alltheongoingeffortsinvolvesomecombinationofdatabase,transaction,encryption,consensusandotherdistributedsystemstechnologies.Someoftheapplicationareasinwhichblockchainpilotsarebeingcarriedoutare:smartcontracts,supplychainmanagement,knowyourcustomer,derivativesprocessingandprovenancemanagement.Inthistalk,Iwillsurveysomeoftheongoingblockchainprojectswithrespecttotheirarchitecturesingeneralandtheirapproachestosomespecifictechnicalareas.Iwillfocusonhowthefunctionalityoftraditionalandmoderndatastoresarebeingutilizedornotutilizedinthedifferentblockchainprojects.Iwillalsodistinguishhowtraditionaldistributeddatabasemanagementsystemshavehandledreplicationandhowblockchainsystemsdoit.Sincemostoftheblockchaineffortsarestillinanascentstate,thetimeisrightfordatabaseandotherdistributedsystemsresearchersandpractitionerstogetmoredeeplyinvolvedtofocusonthenumerousopenproblems.

Bio:Dr.C.MohanhasbeenanIBMresearcherfor35yearsinthedatabasearea,impactingnumerousIBMandnon-IBMproducts,theresearchandacademiccommunities,andstandards,especiallywithhisinventionoftheARIESfamilyofdatabaselockingandrecoveryalgorithms,andthePresumedAbortcommitprotocol.ThisIBM(1997),andACM/IEEE(2002)FellowhasalsoservedastheIBMIndiaChiefScientistfor3years(2006-2009).InadditiontoreceivingtheACMSIGMODInnovationAward(1996),theVLDB10YearBestPaperAward(1999)andnumerousIBMawards,MohanwaselectedtotheUSandIndianNationalAcademiesofEngineering(2009),andwasnamedanIBMMasterInventor(1997).ThisDistinguishedAlumnusofIITMadras(1977)receivedhisPhDattheUniversityofTexasatAustin(1981).Heisaninventorof50patents.HeiscurrentlyfocusedonBigData,HTAPandBlockchaintechnologies.In2016,hewasnamedaDistinguishedVisitingProfessorofChina’sprestigiousTsinghuaUniversity.HehasservedontheadvisoryboardofIEEESpectrum,andonnumerousconferenceandjournalboards.MohanisafrequentspeakerinNorthAmerica,EuropeandIndia,andhasgiventalksin40countries.Heisveryactiveonsocialmediaandhasahugenetworkoffollowers.MoreinformationcouldbefoundintheWikipediapageathttp://bit.ly/CMwIkP

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Keynote 2: M. Tamer Özsu (University of Waterloo)

WebDataManagementintheRDFAge

Webdatamanagementhasbeenatopicofinterestformanyyearsduringwhichanumberofdifferentmodellingapproacheshavebeentried.ThelatestinthisapproachesistouseRDF(ResourceDescriptionFramework),whichseemstoproviderealopportunityforqueryingatleastsomeofthewebdatasystematically.RDFhasbeenproposedbytheWorldWideWebConsortium(W3C)formodelingWebobjectsaspartofdevelopingthe“semanticweb”.W3ChasalsoproposedSPARQLasthequerylanguageforaccessingRDFdatarepositories.ThepublicationofLinkedOpenData(LOD)ontheWebhasgainedtremendousmomentumoverthelastnumberofyears,andthisprovidesanewopportunitytoaccomplishwebdataintegration.AnumberofapproacheshavebeenproposedforrunningSPARQLqueriesoverRDF-encodedWebdata:datawarehousing,SPARQLfederation,andlivelinkedqueryexecution.Inthistalk,IwillreviewtheseapproacheswithparticularemphasisonsomeofourresearchwithinthecontextofgStoreproject(jointprojectwithProf.LeiZouofPekingUniversityandProf.LeiChenofHongKongUniversityofScienceandTechnology),chameleon-dbproject(jointworkwithGünesAluç,Dr.OlafHartig,andProf.KhuzaimaDaudjeeofUniversityofWaterloo),andlivelinkedqueryexecution(jointworkwithDr.OlafHartig).

Bio:M.TamerÖzsuisProfessorofComputerScienceattheDavidR.CheritonSchoolofComputerScience.Hisresearchisindatamanagementfocusingonlarge-scaledatadistributionandmanagementofnon-traditionaldata.HeisaFellowoftheRoyalSocietyofCanada,oftheAssociationforComputingMachinery(ACM),andoftheInstituteofElectricalandElectronicsEngineers(IEEE).HeisanelectedmemberoftheScienceAcademyofTurkey,andamemberofSigmaXiandAmericanAssociationfortheAdvancementofScience(AAAS).HispublicationsincludethebookPrinciplesofDistributedDatabaseSystems(withPatrickValduriez),whichisnowinitsthirdedition.Hehasalsoedited,withLingLiu,theEncyclopediaofDatabaseSystems,whichisnowinitssecondedition.HewastheFoundingSeriesEditorofSynthesisLecturesonDataManagement(Morgan&Claypool),andisnowtheEditor-in-ChiefofACMBooks.Heservesontheeditorialboardsofthreejournals,andtwobookSeries.Prof.Dr.M.TamerÖzsuwasawardedtheACMSIGMODTest-of-TimeAwardin2015,ACMSIGMODContributionsAwardin2008andtheOhioStateUniversityCollegeofEngineeringDistinguishedAlumnusAwardin2008.

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Keynote 3: Kenneth Calvert (National Science Foundation)

APerspectiveonDistributedComputingSystems:Past,PresentandFuture

ThiskeynotetalkwillofferabroadviewofdistributedcomputingfromtheperspectiveofpastandpresentNSFinvestments.Itwillincludealooktowardfuturechallengesandopportunitiesfacingthedistributedcomputingresearchcommunity.

Bio:Dr.KennethCalvertisDivisionDirectorforComputerandNetworkSystemsintheComputerandInformationScienceandEngineering(CISE)DirectorateattheNationalScienceFoundation.HeisonrotationfromtheUniversityofKentucky,whereheisGartnerGroupProfessorinNetworkEngineeringintheDepartmentofComputerScience.Hisresearchdealswiththedesignandimplementationofadvancednetworkprotocolsandservices,withparticularinterestinroutingandincentivesinfuturenetworkarchitectures.HereceivedhisPh.D.incomputersciencefromtheUniversityofTexasatAustin.HeholdsaM.S.incomputersciencefromStanfordUniversityandaB.S.incomputerscienceandengineeringfromtheMassachusettsInstituteofTechnology.PriortohisappointmentattheUniversityofKentucky,hewasaMemberoftheTechnicalStaffatBellLaboratoriesinHolmdel,NJ,andservedonthefacultyintheCollegeofComputingattheGeorgiaInstituteofTechnology.HeisanIEEEFellowandamemberoftheACM.

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Program at a Glance

Day1:Monday,June5,2017

Time Track1(SalonII)

Track2(SalonIV)

Track3(SalonVI)

Track4(Atlanta)

Track5(Columbia)

Track6(Savannah)

Track7(Charleston1)

7:00-8:00 ContinentalBreakfast(Foyer)8:00-9:30

Workshop:CCN-CPS Workshop:

HotPOSTWorkshop:

PSBDWorkshop:

JCC

Workshop:NSF-JSTDay1

Tutorial1:Serverless

Programming(Functionasa

Service)9:30-10:00 CoffeeBreak(Foyer)10:00-12:00

Workshop:CCN-CPS

Workshop:ADSN

Workshop:HotPOST

Workshop:PSBD

Workshop:JCC


Tutorial1:Serverless

Programming(Functionasa

Service)12:00-13:30 Lunch(PhoenixBallroom)13:30-15:30 Workshop:

CCN-CPSWorkshop:

ADSNWorkshop:PED-BGP

Workshop:IoTCA

Workshop:WoSC


15:30-16:00 CoffeeBreak(Foyer)

16:00-17:00Workshop:CCN-CPS

Workshop:ADSN

Workshop:PED-BGP

Workshop:IoTCA

Workshop:WoSC


17:00-18:00 Workshop:

PED-BGP

Day2:Tuesday,June6,2017

Time Track1(SalonI)

Track2(SalonII)

Track3(Atlanta)

Track4(Columbia)

Track5(Nashville)

Track6(Charleston1)

7:00-8:00 ContinentalBreakfast(Foyer)8:00-8:30 ConferenceOpening(PhoenixBallroom)8:30-9:30 Keynote1(PhoenixBallroom)9:30-10:00 CoffeeBreak(Foyer)

10:00-12:00Research1:

DistributedFaultToleranceandDependability

Research2:DistributedOperating

SystemsandMiddleware

Vision1:InternetofThings,SmartCitiesandCyber-Physical

Systems

Application1:Security,Privacy,TrustinDistributedSystems

Application2:SocialNetworksand

Crowdsourcing

Industry1:CloudDataCentersandPerformance

12:00-13:30 Lunch(PhoenixBallroom)

13:30-15:30Research3:

SecurityandPrivacyinDistributedSystemsI

Research4:CloudComputingandDataCenterSystems

Vision2:FutureNetworking

andCyberinfrastructure

Application3:InternetofThings,SmartCities,andCyber-Physical

Systems

Application4:Mobile,Wireless,and

EdgeComputing

ShortPaper1:DistributedOperatingSystems,Middleware,

andAlgorithms

15:30-16:00 CoffeeBreak(Foyer)

16:00-18:00Research5:EdgeandFogComputing

Research6:DistributedGreen

ComputingandEnergyManagement

Vision3:NextGenerationCloudandEdge

Services

ShortPaper2:CloudandDataCenterSystemsandNetworks

ShortPaper3:InternetofThings,SmartCities,andCyber-Physical

Systems

Poster1–4:16:00-17:00Lighteningtalks

(Charleston1&2)17:00-19:00

Display(PhoenixBallroom)

10:00-18:00 The1stUS-JapanWorkshoponCollaborativeGlobalResearchonApplyingInformationTechnologyDay2(Savannah)

18:00-20:00 Reception(PhoenixBallroom)

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Day3:Wednesday,June7,2017

Time Track1(SalonI)

Track2(SalonII)

Track3(Atlanta)

Track4(Columbia)

Track5(Nashville)

Track6(Charleston1)

7:30-8:30 ContinentalBreakfast(Foyer)8:30-9:30 Keynote2(PhoenixBallroom)9:30-10:00 CoffeeBreak(Foyer)

10:00-12:00

Research7:InternetofThings,SmartCities,andCyber-Physical

Systems

Research8:MobileandWirelessComputingSystemsI

Vision4:SecurityandTrustin

FutureSystems

Application5:CloudComputingandDataCenterSystems

Application6:BigDataSystemsandDistributedDataManagementand

Analytics

Industry2:MobileComputingand

InternetofThings

12:00-13:30 ConferenceLuncheon(PhoenixBallroom)

13:30-15:30Research9:

DistributedBigDataSystems

Research10:DistributedAlgorithms

andTheoryI

Vision5:FutureDistributed

Systems

Application7:Distributed

MiddlewareSystems

Application8:DistributedSystemsandOptimizations

ShortPaper4:Mobile,Wireless,Edge,andCrowd

Computing15:30-16:00 CoffeeBreak(Foyer)

16:00-18:00Research11:

SecurityandPrivacyinDistributedSystemsII

Research12:CloudComputingand

DistributedDataAnalytics

Vision6:InnovationinBigData

Systems

Research13:DistributedAlgorithms

andTheoryII

ShortPaper5:DistributedBigDataSystemsandAnalytics

Demo1–4(Charleston1&2)

19:00-21:00 OrganizationEvent(InvitationOnly)

Day4:Thursday,June8,2017

Time Track1(SalonI)

Track2(SalonII)

Track3(Atlanta)

Track4(Columbia)

Track5(Nashville)

Track6(Charleston1)

7:30-8:30 ContinentalBreakfast(Foyer)8:30-9:30 Keynote3(PhoenixBallroom)9:30-10:00 CoffeeBreak(Foyer)

10:00-12:00Research14:

MobileandWirelessComputingSystemsII

Research15:SocialNetworksand

Crowdsourcing

Application9:DistributedSystemsandApplications

Application10:DistributedSystems

andServices

ShortPaper6:Security,Privacy,Trust,andFaultTolerancein

DistributedSystems

Tutorial2:SensorCloud:ACloudofSensor

Networks

12:00-13:30

BusinessLunchincludingAwardsandICDCS2018Announcements

(PhoenixBallroom)

13:30-15:30PlenaryPanel&

ConferenceClosingRemarks(PhoenixBallroom)

Tutorial2:SensorCloud:ACloudofSensor

Networks

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Day 1 – Monday, June 5, 2017

TenWorkshopsandtwotutorials(seeProgramataglanceandworkshopswebsiteandICDCS2017websitefordetails)

7:00-8:00 Monday, June 5, 2017 ContinentalBreakfastLocation:Foyer

8:00-9:30 Monday, June 5, 2017 Tutorial1:ServerlessProgramming(FunctionasaService)Location:Charleston1

PaulCastro(IBMT.J.WatsonResearchCenter),VatcheIshakian(BentleyUniversity),VinodMuthusamy(IBMT.J.WatsonResearchCenter),AleksanderSlominski(IBMT.J.WatsonResearchCenter)

Workshop:CCN-CPS,Session1Location:SalonII

SessionChair:NaderMohamed(MiddlewareTechnologiesLab.)

PoliciesGuidingCohesiveInteractionsamongInternetofThingswithCommunicationCloudandSocialNetworks HenryHexmoor(SouthernIllinoisUniversity)

EnhancedSecurityofBuildingAutomationSystemsThroughMicrokernel-BasedControllerPlatforms XiaolongWang(UniversityofSouthFlorida),RichardHabeeb(UniversityofSouthFlorida),XinmingOu(UniversityofSouthFlorida),SiddharthAmaravadi(KansasStateUniversity),JohnHatcliff(KansasStateUniversity),MasaakiMizuno(KansasStateUniversity),MitchellLNeilsen(KansasStateUniversity),RajRajagopalan(Honeywell),SrivatsanVaradarajan(HoneywellAerospaceAdvancedTechnologyLabs)

HighlevelDesignofaHomeAutonomousSystemBasedonCyberPhysicalSystemModeling BasmanAlhafidh(FloridaInstituteofTechnology),WilliamH.Allen(FloridaInstituteofTechnology)

Workshop:HotPOST,Session1Location:SalonVI

KeynoteSpeech:AMarkovGameTheoreticApproachforPowerGridSecurity CharlesA.Kamhoua(AirForceResearchLaboratory)

Router-basedBrokeringforSurrogateDiscoveryinEdgeComputing JulienGedeon(TechnischeUniversitätDarmstadt),ChristianMeurisch(TechnischeUniversitätDarmstadt),DishaBhat(TechnischeUniversitätDarmstadt),MichaelStein(TechnischeUniversitätDarmstadt),LinWang(TechnischeUniversitätDarmstadt),MaxMühlhäuser(TechnischeUniversitätDarmstadt)

ModelingtheSpreadofInfluenceforIndependentCascadeDiffusionProcessinSocialNetworks ZeshengChen(IndianaUniversity-PurdueUniversityFortWayne),KurtisTaylor(IndianaUniversity-PurdueUniversityFortWayne)

Workshop:PSBD,OpeningandInvitedTalksLocation:Atlanta

KeynoteSpeech:BigData-SecurityandPrivacy(andTransparency) ElisaBertino(PurdueUniversity)

InvitedTalk:SupportingTime-varyingPrivacywithSelf-emergingData BalajiPalanisamy(UniversityofPittsburgh)

Workshop:JCC,Session1Location:Columbia

HeterogeneousMalwareSpreadProcessinStarNetwork LiboJiao(TsinghuaUniversity),HaoYin(TsinghuaUniversity),DongchaoGuo(TsinghuaUniversity),YongqiangLyu(TsinghuaUniversity)

CostReductioninHybridCloudsforEnterpriseComputing

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BiyuZhou(InstituteofComputingTechnology,ChineseAcademyofSciences),FaZhang(InstituteofComputingTechnology,ChineseAcademyofSciences),JieWu(TempleUniversity),ZhiyongLiu(InstituteofComputingTechnology,ChineseAcademyofSciences)

DC-RSF:ADynamicandCustomizedReputationSystemFrameworkforJointCloudComputingFanghuaYe(SunYat-senUniversity),ZibinZheng(SunYat-senUniversity),ChuanChen(SunYat-senUniversity),YurenZhou(SunYat-senUniversity)

WebServiceApplianceBasedonUnikernel KaiYu(NationalLabforParallelandDistributedProcessing),ChengfeiZhang(NationalLabforParallelandDistributedProcessing),YunxiangZhao(NationalLabforParallelandDistributedProcessing)

AnalysisandEvaluationoftheGASModelforDistributedGraphComputation WangJinyan(NationalLabforParallelandDistributedProcessing),ZhangChengfei(NationalLabforParallelandDistributedProcessing)

TrafficSignsDetectionBasedonFasterR-CNN ZhongrongZuo(NationalLabforParallelandDistributedProcessing),KaiYu(NationalLabforParallelandDistributedProcessing),QiaoZhou(NationalLabforParallelandDistributedProcessing),XuWang(NationalLabforParallelandDistributedProcessing),TingLi(NationalLabforParallelandDistributedProcessing)

JCLedger:ABlockchainBasedDistributedLedgerforJointCloudComputing XiangFu(NationalUniversityofDefenseTechnology),HuaiminWang(NationalUniversityofDefenseTechnology),PeichangShi(NationalUniversityofDefenseTechnology),YingweiFu(NationalUniversityofDefenseTechnology),YijieWang(NationalUniversityofDefenseTechnology)

CorporationArchitectureforMultipleCloudServiceProvidersinJointCloudComputing PeichangShi(NationalUniversityofDefenseTechnology),HuaiminWang(NationalUniversityofDefenseTechnology),XikunYue(NationalUniversityofDefenseTechnology),ShilanYang(NationalUniversityofDefenseTechnology),ShangzhiYang(NationalUniversityofDefenseTechnology),YuxingPeng(NationalUniversityofDefenseTechnology)

SharingPrivacyDatainSemi-TrustworthyStoragethroughHierarchicalAccessControl YuzhaoWu(TsinghuaUniversity),YongqiangLyu(TsinghuaUniversity),QianFang(TsinghuaUniversity),GengZheng(TsinghuaUniversity),HaoYin(TsinghuaUniversity),YuanchunShi(TsinghuaUniversity)

Workshop:NSF-JSTLocation:Savannah

9:30-10:00 Monday, June 5, 2017 CoffeeBreakLocation:PhoenixBallroom

10:00-12:00 Monday, June 5, 2017 Tutorial1:ServerlessProgramming(FunctionasaService)Location:Charleston1

PaulCastro(IBMT.J.WatsonResearchCenter),VatcheIshakian(BentleyUniversity),VinodMuthusamy(IBMT.J.WatsonResearchCenter),AleksanderSlominski(IBMT.J.WatsonResearchCenter)

Workshop:CCN-CPS,Session2Location:SalonII

SessionChair:JameelaAlJaroodi(RobertMorrisUniversity)

ACyberPhysicalBuses-and-DronesMobileEdgeInfrastructureforLargeScaleDisasterEmergencyCommunications MamtaNarang(AucklandUniversityofTechnology),WilliamLiu(AucklandUniversityofTechnology),JairoAGutierrez(AucklandUniversityofTechnology),LucaChiaraviglio(UniversityofRomeTorVergata)

APerformanceComparisonofContainersandVirtualMachinesinWorkloadMigrationContext KumarGaurav(VMwareSoftwareIndiaPvtLtd),PavanKarkun(VMwareSoftwareIndiapvtLTD),Y.C.Tay(NationalUniversityofSingapore)

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TowardsService-OrientedMiddlewareforCyberPhysicalSystems NaderMohamed(MiddlewareTechnologiesLab.),SanjaLazarova-Molnar(UniversityofSouthernDenmark)

NetworkingandCommunicationinCyberPhysicalSystems ImadJawhar(UAEUniversity),JameelaAl-Jaroodi(RobertMorrisUniversity)

Workshop:ADSN,Session1:Keynote,Session2:AssuringTemporalFairnessandSecuringCommunicationLocation:SalonIV

KeynoteSpeech:DependabilityChallengesin5GCellularNetworks DouglasM.Blough(GeorgiaInstituteofTechnology)

UnderstandingandImprovingTemporalFairnessonanElectronicTradingVenue HaydenMelton(DeakinUniversity)

CertificateLessCryptography-basedRuleManagementProtocolforAdvancedMissionDeliveryNetworks JonghoWon(PurdueUniversity),AnkushSingla(PurdueUniversity),ElisaBertino(PurdueUniversity)

Workshop:HotPOST,Session2Location:SalonVI

ThankYouForBeingAFriend:AnAttackerViewonOnline-Social-Network-basedSybilDefenses DavidKoll(UniversityofGoettingen),MartinSchwarzmaier(UniversityofGoettingen),JunLi(UniversityofOregon),Xiang-YangLi(UniversityofScienceandTechnologyofChina),XiaomingFu(UniversityofGoettingen)

EfficientDynamicServiceFunctionChainCombinationofNetworkFunctionVirtualization WenkeYan(BeijingUniversityofPostsandTelecommunications),KonglinZhu(BeijingUniversityofPostsandTelecommunications),LinZhang(BeijingUniversityofPostsandTelecommunications),SixiSu(BeijingUniversityofPostsandTelecommunications)

WhenAugmentedRealitymeetsBigData CarlosBermejo(TheHongKongUniversityofScienceandTechnology),ZhanpengHuang(TheHongKongUniversityofScienceandTechnology),TristanBraud(TheHongKongUniversityofScienceandTechnology),PanHui(TheHongKongUniversityofScienceandTechnology)

SamplingBasedEfficientAlgorithmtoEstimatetheSpectralRadiusofLargeGraphs SamarAbbas(LahoreUniversityofManagementSciences),JuvariaTariq(LahoreUniversityofManagementSciences),ArifZaman(LahoreUniversityofManagementSciences),ImdadullahKhan(LahoreUniversityofManagementSciences)

ExtemporaneousMicro-MobileServiceExecutionWithoutCodeSharing ZhengSong(VirginiaTech),MinhLe(UtahStateUniversity),Young-WooKwon(UtahStateUniversity),EliTilevich(VirginiaTech)

PreventingColludingIdentityCloneAttacksinOnlineSocialNetworks GeorgesA.Kamhoua(FloridaInternationalUniversity),NikiPissinou(FloridaInternationalUniversity),S.S.Iyengar(FloridaInternationalUniversity),JonathanBeltran(FloridaInternationalUniversity),CharlesKamhoua(AirForceResearchLaboratory),BrandonLHernandez(UTRGV),LaurentNjilla(AirForceResearchLaboratory)

Workshop:PSBD,ResearchSessionLocation:Atlanta

Anovelgame-theoreticmodelforcontent-adaptiveimagesteganography QiLi(HunanUniversity),XinLiao(HunanUniversity),GuoyongChen(HunanUniversity),LipingDing(GuangzhouBranchofInstituteofSoftware,ChineseAcademyofScience)

AFine-grainedAccessControlSchemeforBigDataBasedonClassificationAttributes TengfeiYang(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences),PeisongShen(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences),XueTian(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences),ChiChen(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences)

Social-AwareDecentralizationforEfficientandSecureMulti-PartyComputation YuzheTang(SyracuseUniversity),SuchetaSoundarajan(SyracuseUniversity)

StatisticalAnomalyDetectiononMetadataStreamsviaCommoditySoftwaretoProtectCompany ChristineChen(UniversityofPortland),JamesGurganus(MicroSystemsEngineering,Inc.)

Computationalimprovementsinparallelizedk-anonymousmicroaggregationoflargedatabases AhmadMohamadMezher(UniversitatPolitècnicadeCatalunya(UPC)),AlejandroGarcíaÁlvarez(UniversitatPolitècnicadeCatalunya(UPC)),DavidRebollo-Monedero(UniversitatPolitècnicadeCatalunya(UPC)),JordiForné(UniversitatPolitècnicadeCatalunya(UPC))

24

Workshop:JCC,Session2Location:Columbia

AReliabilityBenchmarkforBigDataSystemsonJointCloudYingyingZheng(InstituteofSoftware,ChineseAcademyofSciences),LijieXu(InstituteofSoftware,ChineseAcademyofSciences),WeiWang(InstituteofSoftware,ChineseAcademyofSciences),WeiZhou(KSYUN),YingDing(ChangchunUniversityofScienceandTechnology)

UCPR:UserClassificationandInfluenceAnalysisinSocialNetwork CongZha(TsinghuaUniversity),YongqiangLv(TsinghuaUniversity)

AdaptiveRoutingAlgorithmforJointCloudVideoDelivery ZexunJiang(TsinghuaUniversity),HaoYin(TsinghuaUniversity)

TowardsEfficientResourceManagementinVirtualClouds BoAn(PekingUniversity),JunmingMa(PekingUniversity),DonggangCao(PekingUniversity),GangHuang(PekingUniversity)

MonitoringandBillingofALightweightCloudSystemBasedonLinuxContainer YujianZhu(PekingUniversity),JunmingMa(PekingUniversity),BoAn(PekingUniversity),DonggangCao(PekingUniversity)

Buildingemulationframeworkfornon-volatilememory GuoliangZhu(NationalUniversityofDefenseTechnology),KaiLu(NationalUniversityofDefenseTechnology),XiaopingWang(NationalUniversityofDefenseTechnology)

Seflow:EfficientFlowSchedulingforData-ParallelJobs QiaoZhou(NationalLabforParallelandDistributedProcessing),ZiyangLi(NationalLabforParallelandDistributedProcessing),PingZhong(CentralSouthUniversity),TianTian(NationalLabforParallelandDistributedProcessing),YuxingPeng(NationalLabforParallelandDistributedProcessing)

OnlineEncodingforErasure-CodedDistributedStorageSystems FangliangXu(NationalUniversityofDefenseTechnology),YijieWang(NationalUniversityofDefenseTechnology),XingkongMa(NationalUniversityofDefenseTechnology)

Workshop:NSF-JST,Session1Location:Savannah

AcceleratingBigDataInfrastructureandApplications KevinBrown(TokyoInstituteofTechnology),TianqiXu(TokyoInstituteofTechnology),KeitaIwabuchi(TokyoInstituteofTechnology),KentoSato(LawrenceLivermoreNationalLaboratory),AdamMoody(LawrenceLivermoreNationalLaboratory),KathrynMohror(LawrenceLivermoreNationalLaboratory),NikhilJain(LawrenceLivermoreNationalLaboratory),AbhinavBhatele(LawrenceLivermoreNationalLaboratory),MartinSchulz(LawrenceLivermoreNationalLaboratory),RogerPearce(LawrenceLivermoreNationalLaboratory),MayaGokhale(LawrenceLivermoreNationalLaboratory),SatoshiMatsuoka(TokyoInstituteofTechnology)

DisasterNetworkEvolutionUsingDynamicClusteringofTwitterData KrishnaKant(TempleUniversity),YilangWu(AizuUniversity),ShanshanZhang(TempleUniversity),JunboWang(AizuUniversity),AmitangshuPal(TempleUniversity)

Single-epochsupernovaclassificationwithdeepconvolutionalneuralnetworks AkisatoKimura(NTT),IchiroTakahashi(KavliIPMU,TheUniversityofTokyo),MasaomiTanaka(NationalAstronomicalObservatoryofJapan),NaokiYasuda(KavliIPMU,TheUniversityofTokyo),NaonoriUeda(NTT),NaokiYoshida(KavliIPMU,TheUniversityofTokyo)

EnablingLargeScaleDeliberationusingIdeationandNegotiation-SupportAgents KatsuhideFujita(TokyoUniversityofAgricultureandTechnology),TakayukiIto(NagoyaInstituteofTechnology),MarkKlein(MIT)

12:00-13:30 Monday, June 5, 2017 LunchLocation:Foyer

13:30-15:30 Monday, June 5, 2017 Workshop:CCN-CPS,Session3Location:SalonII

SessionChair:UttamGhosh(TennesseeStateUniversity)

OptimalDeploymentofChargingStationsforElectricVehicles:AFormalApproach

25

AmarjitDatta(TennesseeTechnologicalUniversity),BrianLedbetter(TennesseeTechnologicalUniversity),MohammadAshiqurRahman(TennesseeTechnologicalUniversity)

FormalVerificationofControlStrategiesforaCyberPhysicalSystemAmjadGawanmeh(KhalifaUniversityofScienceandTechnology),AliAlwadi(AucklandUniversityofTechnology),SaziaParvin(UniversityofNewSouthWales)

LightweightDetectionandIsolationofBlackHoleAttacksinConnectedVehicles SamiAlbouq(OaklandUniversity),ErikFredericks(OaklandUniversity)

AnewthreatassessmentmethodforintegratinganIoTinfrastructureinaninformationsystem BrunoDorsemaine(OrangeLabs),Jean-PhilippeGaulier(OrangeLabs),Jean-PhilippeWary(OrangeLabs),NizarKheir(Thales),PascalUrien(TelecomParisTech)

Workshop:ADSN,Session3:NetworkAssuranceLocation:SalonIV

FaultySensorDataDetectioninWirelessSensorNetworksUsingLogisticalRegressionTianyuZhang(UniversityofHyogo),QianZhao(UniversityofHyogo),YukikazuNakamoto(UniversityofHyogo)

AnAdaptability-EnhancedRoutingMethodforMultipleGateway-basedWirelessSensorNetworksUsingSecureDispersedDataTransfer RyumaTani(HiroshimaCityUniversity),KentoAoi(HiroshimaCityUniversity),EitaroKohno(HiroshimaCityUniversity),YoshiakiKakuda(HiroshimaCityUniversity)

ProgressiveDownloadMethodBasedonTimer-DrivenRequestingSchemesUsingMultipleTCPFlowsonMultiplePaths HiroakiHoriba(HiroshimaCityUniversity),TokumasaHiraoka(HiroshimaCityUniversity),JunichiFunasaka(HiroshimaCityUniversity)

Workshop:PED-BGP,Session1Location:SalonVI

Keynotespeech:Application-awaredatadissemination BettinaKemme(McGillUniversity)

WED-SQL:ARelationalFrameworkforDesignandImplementationofProcess-AwareInformationSystems BrunoPadilha(UniversityofSaoPaulo),AndréLuisSchwerz(FederalUniversityofTechnology),RafaelLiberatoRoberto(FederalUniversityofTechnology)

QueryingWorkflowLogsYanTang(UniversityofCaliforniaatSantaBarbara),JianwenSu(UniversityofCaliforniaatSantaBarbara)

Ontheintegrationofevent-basedandtransaction-basedarchitecturesforSupplyChains ZhijieLi(IndianaUniversity–PurdueUniversityIndianapolis),HaoyanWu(IndianaUniversity–PurdueUniversityIndianapolis),BrianKing(IndianaUniversity–PurdueUniversityIndianapolis),ZinaBen-Miled(IndianaUniversity–PurdueUniversityIndianapolis),JohnWassick(TheDowChemicalCompany),JeffreyTazelaar(TheDowChemicalCompany)

Workshop:IoTCA,Session1Location:Atlanta

KeynoteSpeech:TheInternetofThings,People,andSystems:FromtheEdgetotheCloud SchahramDustdar(TUWien)

TowardsPrivacy-AwareSmartBuildings:Capturing,Communicating,andEnforcingPrivacyPoliciesandPreferences PrimalPappachan(UniversityofCaliforniaIrvine),MartinDegelingy(CarnegieMellonUniversity),RobertoYus(UniversityofCaliforniaIrvine),AnupamDasy(CarnegieMellonUniversity),SrutiBhagavatulay(CarnegieMellonUniversity),WilliamMelichery(CarnegieMellonUniversity),PardisEmamiNaeiniy(CarnegieMellonUniversity),ShikunZhangy(CarnegieMellonUniversity),LujoBauery(CarnegieMellonUniversity),AlfredKobsa(UniversityofCaliforniaIrvine),SharadMehrotra(UniversityofCaliforniaIrvine),NormanSadeh(CarnegieMellonUniversity),NaliniVenkatasubramanian(UniversityofCaliforniaIrvine)

DeployingData-DrivenSecuritySolutionsonResource-ConstrainedWearableIoTSystem HangCai(WorcesterPolytechnicInstitute),TianlongYun(WorcesterPolytechnicInstitute),JosiahHester(DartmouthCollege),KrishnaK.Venkatasubramanian(ClemsonUniversity)

AMotifbasedIoTFrameworkforDataEfficiency AkashSahoo(TexasA&MUniversity),RabiMahapatra(TexasA&MUniversity)

Workshop:WoSC,Session1

26

Location:Columbia

KeynoteSpeech:ServerlessComputing:PatternsandRoadAhead RogerBarga(AmazonWebServices)

Ripple:HomeAutomationforResearchDataManagement RyanChard(ArgonneNationalLaboratory),KyleChard(ComputationInstitute,UniversityofChicagoandArgonneNationalLab),JasonAlt(NationalCenterforSupercomputingApplications),DilworthParkinson(LawrenceBerkeleyNationalLaboratory),SteveTuecke(ComputationInstitute,UniversityofChicagoandArgonneNationalLab),IanFoster(ArgonneNationalLaboratory&TheUniversityofChicago)

Pipsqueak:LeanLambdaswithLargeLibraries EdwardOakes(UniversityofWisconsin-Madison),LeonYang(UniversityofWisconsin-Madison),KevinHouck(UniversityofWisconsin-Madison),TylerHarter(MicrosoftGraySystemsLab),AndreaC.Arpaci-Dusseau(UniversityofWisconsin-Madison),RemziH.Arpaci-Dusseau(UniversityofWisconsin-Madison)

LeveragingtheServerlessArchitectureforSecuringLinuxContainersNiltonBila(IBM),PaoloDettori(IBM),AliKanso(IBM),YujiWatanabe(IBM),AlaaYoussef(IBM)


15:30-16:00 Monday, June 5, 2017 CoffeeBreakLocation:Foyer

16:00-17:00 Monday, June 5, 2017 Workshop:CCN-CPS,Session4Location:SalonII

SessionChair:BrunoDorsemaine(OrangeLabs)

ASecurityFrameworkforSDN-enabledSmartPowerGrids UttamGhosh(TennesseeStateUniversity),PushpitaChatterjee(SRMRESEARCHINSTITUTE),SachinShetty(OldDominionUniversity)

Real-timeMonitoringSteamGeneratorsusingaHybridImagingSystem MahmoudMeribout(PetroleumInstitute),ImranSaied(PetroleumInstitute),EsraAlHosani(AdcoGroup)

SecuringbigDataEfficientlythroughMicroaggregationTechniqueandHuffmanCompression ShakilaMahjabinTonni(BangladeshArmyInternationalUniversityofScienceandTechnology),MohammadZahidurRahman(JahangirnagarUniversity),SaziaParvin(UniversityofNewSouthWales),AmjadGawanmeh(KhalifaUniversityofScienceandTechnology)

ModelBasedEnergyConsumptionAnalysisofWirelessCyberPhysicalSystems JingLiu(PekingUniversity),PingWang(PekingUniversity),JinlongLin(PekingUniversity),Chao-HsienChu(PennsylvanniaStateUniversity)

Workshop:ADSN,Session4:Panelon“AssuranceinInternetofThings(IoT)”Location:SalonIV

Moderator:EitaroKohno

Workshop:PED-BGP,Session2Location:SalonVI

CacheDOCS:ADynamicKey-ValueObjectCachingService JulienGascon-Samson(UniversityofBritishColumbia),MichaelCoppinger(McGillUniversity),FanJin(McGillUniversity),JörgKienzle(McGillUniversity),BettinaKemme(McGillUniversity)

WolfPath:Acceleratingiterativetraversing-basedgraphprocessingalgorithmsonGPU HuanzhouZhu(UniversityofWarwick),LigangHe(UniversityofWarwick)

ANovelAuction-basedQueryPricingSchema XingwangWang(JilinUniversity),XiaohuiWei(JilinUniversity),ShangGao(JilinUniversity),YuanyuanLiu(JilinUniversity),ZongpengLi(UniversityofCalgary)

BlockGraphChi:EnablingBlockUpdateinOut-of-coreGraphProcessing

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ZhiyuanShao(HuazhongUniversityofScienceandTechnology),ZhenjieMei(HuazhongUniversityofScienceandTechnology),XiaofengDing(HuazhongUniversityofScienceandTechnology),HaiJin(HuazhongUniversityofScienceandTechnology)

Workshop:IoTCA,Session2Location:Atlanta

CoTWare:ACloudofThingsMiddleware JameelaAl-Jaroodi(RobertMorrisUniversity),NaderMohamed(MiddlewareTechnologiesLab.),ImadJawhar(MidcompResearchCenter)

SecuringtheInternetofThings:AMeta-StudyofChallenges,Approaches,andOpenProblemsMahmudHossain(UniversityofAlabamaatBirmingham),RagibHasan(UniversityofAlabamaatBirmingham),AnthonySkjellum(AuburnUniversity)

InternetofThingsFrameworkforSmartLearningAnalyticsAliYavari(SwinburneUniversityofTechnology),RezaSoltanpoor(RMITUniversity)

Workshop:WoSC,Session2Location:Columbia

ServerlessComputing:Design,Implementation,andPerformance GarrettMcGrath(UniversityofNotreDame),PaulR.Brenner(UniversityofNotreDame)

PaneldebateonthenoveltyandchallengesofserverlesscomputingParticipants:TBA


17:00-18:00 Monday, June 5, 2017 Workshop:PED-BGP,Session2Location:SalonVI

IncrementalParallelComputingusingTransactionalModelinLarge-scaleDynamicGraphStructures AnandTripathi(UniversityofMinnesota),RahulR.Sharma(UniversityofMinnesota),ManuKhandelwal(UniversityofMinnesota),TanmayMehta(UniversityofMinnesota),VarunPandey(UniversityofMinnesota)

AgainstSigned-GraphDeanonymizationAttacks:PrivacyProtectionforSocialNetworks JianliangGao(CentralSouthUniversity),YuLiu(CentralSouthUniversity),PingZhong(CentralSouthUniversity),JianxinWang(CentralSouthUniversity)

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Day 2 – Tuesday, June 6, 2017

7:00-8:00 Tuesday, June 6, 2017 ContinentalBreakfastLocation:Foyer

8:00-8:30 Tuesday, June 6, 2017 ConferenceOpeningLocation:PhoenixBallroom

8:30-9:30 Tuesday, June 6, 2017 Keynote1byC.Mohan(IBMResearch)Location:PhoenixBallroom

SessionChair:MasaruKitsuregawa(UniversityofTokyo)

9:30-10:00 Tuesday, June 6, 2017 CoffeeBreakLocation:Foyer

10:00-12:00 Tuesday, June 6, 2017 Research1:DistributedFaultToleranceandDependabilityLocation:SalonI

SessionChair:MudhakarSrivatsa(IBMT.J.WatsonResearchCenter)

Timely,Reliable,andCost-EffectiveInternetTransportServiceusingDisseminationGraphs AmyBabay(JohnsHopkinsUniversity),EmilyWagner(JohnsHopkinsUniversity,LTNGlobalCommunications),MichaelDinitz(JohnsHopkinsUniversity),YairAmir(JohnsHopkinsUniversity,LTNGlobalCommunications)

Pronto:EfficientTestPacketGenerationforDynamicNetworkDataPlanes YuZhao(UniversityofKentucky),HuazheWang(UniversityofCaliforniaatSantaCruz),XinLin(UniversityofCaliforniaatSantaCruz),TingtingYu(UniverstiyofKentucky),ChenQian(UniversityofCaliforniaatSantaCruz)

Agar:ACachingSystemforErasure-CodedData RalucaHalalai(UniversityofNeuchâtel),PascalFelber(UniversityofNeuchâtel),Anne-MarieKermarrec(INRIA),FrançoisTaïani(IRISA)

Highperformancerecoveryforparallelstatemachinereplication OdoricoMendizabal(FURG),FernandoLuisDotti(PUCRS),FernandoPedone(UniversityofLugano)

OnDataParallelismofErasureCodinginDistributedStorageSystems JunLi(UniversityofToronto),BaochunLi(UniversityofToronto)

MeteorShower:MinimizingRequestLatencyforMajorityQuorum-basedDataConsistencyAlgorithmsinMultipleDataCenters YingLiu(KTHRoyalInstituteofTechnology),XiGuan(KTHRoyalInstituteofTechnology),VladimirVlassov(KTHRoyalInstituteofTechnology),SeifHaridi(KTHRoyalInstituteofTechnology)

Research2:DistributedOperatingSystemsandMiddlewareLocation:SalonII

SessionChair:PeterPietzuch(ImperialCollegeLondon)

LSbM-tree:Re-enablinghigh-speedcachinginDataManagementforMixedReadsandWrites DejunTeng(TheOhioStateUniversity),LeiGuo(Google),RubaoLee(TheOhioStateUniversity),FengChen(LouisianaStateUniversity),SiyuanMa(TheOhioStateUniversity),XiaodongZhang(TheOhioStateUniversity),YanfengZhang(NortheasternUniversity)

IncrementalTopologyTransformationforPublish/SubscribeSystemsUsingIntegerProgramming PooyaSalehi(TechnicalUniversityofMunich),KaiwenZhang(TechnicalUniversityofMunich),Hans-ArnoJacobsen(UniversityofToronto)

milliScope:aFine-GrainedMonitoringFrameworkforPerformanceDebuggingofn-TierWebServices

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Chien-AnLai(GeorgiaInstituteofTechnology),JoshKimball(GeorgiaInstituteofTechnology),TaoZhu(GeorgiaInstituteofTechnology),QingyangWang(LouisianaStateUniversity),CaltonPu(GeorgiaInstituteofTechnology)

Stark:OptimizingIn-MemoryComputingForDynamicDatasetCollections ShenLi(IBMResearch),MdTanvirAlAmin(UIUC),RaghuGanti(IBMResearch),MudhakarSrivatsa(IBMResearch),ShaohanHu(IBMResearch),YiranZhao(UIUC),TarekAbdelzaher(UIUC)

CRESON:CallableandReplicatedSharedObjectsoverNoSQL PierreSutra(TélécomSudParis,CNRS,UniversitéParis-Saclay,France),EtienneRivière(UniversityofNeuchatel),CristianCotes(UniversitatRoviraiVirgili),MarcSánchezArtigas(UniversitatRoviraiVirgili),PedroGarciaLopez(UniversitatRoviraiVirgili),EmmanuelBernard(RedHat),WilliamBurns(RedHat),GalderZamarreno(RedHat)

VirtualizedNetworkCodingFunctionsontheInternet LinquanZhang(UniversityofCalgary),ShangqiLai(TheUniversityofHongKong),ChuanWu(TheUniversityofHongKong),ZongpengLi(UniversityofCalgary),ChuanxiongGuo(MicrosoftResearch)

Vision1:InternetofThings,SmartCitiesandCyber-PhysicalSystemsLocation:Atlanta

SessionChair:LingLiu(GeorgiaInstituteofTechnology)

Observable-by-Design MasaruKitsuregawa(NationalInstituteofInformatics(NII)/InstituteofIndustrialScience,UniversityofTokyo)

AnArchitecturalVisionforaData-CentricIoT:RethinkingThings,TrustandClouds EveM.Schooler(Intel),DavidZage(Intel),JeffSedayao(Intel),HassnaaMoustafa(Intel),AndrewBrown(Intel),MorenoAmbrosin(UniversityofPadua)

EdgeComputingandIoTBasedResearchforBuildingSafeSmartCitiesResistanttoDisasters TeruoHigashino(OsakaUniversity),HirozumiYamaguchi(OsakaUniversity),AkihitoHiromori(OsakaUniversity),AkiraUchiyama(OsakaUniversity),KeiichiYasumoto(NaraInstituteofScienceandTechnology)

TheInternetofThingsandMultiagentSystems:DecentralizedIntelligenceinDistributedComputing MunindarSingh(NorthCarolinaStateUniversity),AmitChopra(LancasterUniversity)

InternetofThings:FromSmall-toLarge-ScaleOrchestration CharlesConsel(Inria/BordeauxINP),MilanKabac(ImperialCollege)

EdgeOS_H:AHomeOperatingSystemforInternetofEverything JieCao(WayneStateUniversity),LanyuXu(WayneStateUniversity),RaefAbdallah(WayneStateUniversity),WeisongShi(WayneStateUniversity)

Application1:Security,Privacy,TrustinDistributedSystemsLocation:Columbia

SessionChair:SongqingChen(GeorgeMasonUniversity)

PrivacyPreservingUser-basedRecommenderSystem ShahriarBadsha(RMITUniversity),XunYi(RMITUniversity),IbrahimKhalil(RMITUniversity),ElisaBertino(PurdueUniversity)

PrivacyPreservingOptimizationofParticipatorySensinginMobileCloudComputing YeYan(OaklandUniversity),DongHan(OaklandUniversity),TaoShu(AuburnUniversity)

SPHINX:APasswordStorethatPerfectlyHidesPasswordsfromItself MalihehShirvanian(UniversityofAlabamaatBirmingham),StanislawJarecki(UniversityofCaliforniaatIrvine),HugoKrawczyk(IBMResearch),NiteshSaxena(UniversityofAlabamaatBirmingham)

WhenSmartTVMeetsCRN:Privacy-preservingFine-grainedSpectrumAccess ChaowenGuan(StateUniversityofNewYorkatBuffalo),AzizMohaisen(StateUniversityofNewYorkatBuffalo),ZhiSun(StateUniversityofNewYorkatBuffalo),LuSu(StateUniversityofNewYorkatBuffalo),KuiRen(StateUniversityofNewYorkatBuffalo),YalingYang(VirginiaTech)

RevisitingSecurityRisksofAsymmetricScalarProductPreservingEncryptionandItsVariants WeipengLin(SimonFraserUniveristy),KeWang(SimonFraserUniversity),ZhilinZhang(SimonFraserUniversity),HongChen(RenminUniversityofChina)

AnAdversary-CentricBehaviorModelingofDDoSAttacks AnWang(GeorgeMasonUniversity),AzizMohaisen(SUNYBuffalo),SongqingChen(GeorgeMasonUniversity)

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Application2:SocialNetworksandCrowdsourcingLocation:Nashville

SessionChair:YaoLiu(SUNYBinghamton)

Anti-MaliciousCrowdsourcingUsingtheZero-DeterminantStrategy QinHu(BeijingNormalUniversity),ShenglingWang(BeijingNormalUniversity),LiranMa(TexasChristianUniversity),RongfangBie(BeijingNormalUniversity),XiuzhenCheng(GeorgeWashingtonUniversity)

JPR:ExploringJointPartitioningandReplicationforTrafficMinimizationinOnlineSocialNetworks JingyaZhou(SoochowUniversity),JianxiFan(SoochowUniversity)

OptimizingSourceSelectioninSocialSensinginthePresenceofInfluenceGraphs HuajieShao(UIUC),ShiguangWang(UIUC),ShenLi(UIUC),ShuochaoYao(UIUC),YiranZhao(UIUC),MdTanvirAlAmin(UIUC),TarekAbdelzaher(UIUC),LanceKaplan(UIUC)

DynamicContractDesignforHeterogenousWorkersinCrowdsourcingforQualityControl ChenxiQiu(PennsylvaniaStateUniversity),AnnaSquicciarini(PennsylvaniaStateUniversity),SarahRajtmajer(PennsylvaniaStateUniversity),JamesCaverlee(TexasA&MUniversity)

JointRequestBalancingandContentAggregationinCrowdsourcedCDN MingMa(TsinghuaUniversity),ZhiWang(TsinghuaUniversity),KunYi(TsinghuaUniversity),JiangchuanLiu(SouthChinaAgriculturalUniversity),LifengSun(TsinghuaUniversity)

Shrink:ABreastCancerRiskAssessmentModelBasedonMedicalSocialNetwork AliLi(UniversityofScienceandTechnologyBeijing),RuiWang(UniversityofScienceandTechnologyBeijing),LeiXu(UniversityofScienceandTechnologyBeijing)

Industry1:CloudDataCentersandPerformanceLocation:Charleston1

SessionChair:RaghuGanti(IBMT.J.WatsonResearchCenter)

Phoenix:Constraintawareschedulingforheterogeneousdatacenters PrashanthThinakaran(PennsylvaniaStateUniversity),JashwantRajGunasekaran(PennsylvaniaStateUniversity),BikashSharma(MicrosoftCorp),MahmutKandemir(PennsylvaniaStateUniversity),ChitaDas(PennsylvaniaStateUniversity)

DualScalingVMsandQueries:Cost-effectiveLatencyCurtailment JuanPérez(UniversityofMelbourne),RobertBirke(IBMResearchZurich),MathiasBjörkqvist(IBMResearchZurich),LydiaY.Chen(IBMResearchZurich)

Aframeworkforenablingsecurityservicescollaborationacrossmultipledomains DanielMigault(EricssonSecurityResearch),MarcosSimplicioJunior(EscolaPolitécnica),BrunoBarros(EscolaPolitécnica),MakanPourzandi(EricssonSecurityResearch),ThiagoAlmeida(EscolaPolitécnica),EwertonAndrade(EscolaPolitécnica),TerezaCarvalho(EscolaPolitécnica)

GroupClusteringUsingInter-GroupDissimilarities DebessayFesehayeKassa(VMware),LeninSingaravelu(Google),Chien-ChiaChen(VMware),XiaoboHuang(VMware),AmitabhaBanerjee(VMware),RuijinZhou(VMware),RajeshSomasundaran(VMware)

ComprehensiveMeasurementandAnalysisoftheUser-PerceivedI/OPerformanceinaProductionLeadership-ClassStorageSystemLipengWan(OakRidgeNationalLaboratory),MatthewWolf(OakRidgeNationalLaboratory),FeiyiWang(OakRidgeNationalLaboratory),JongYoulCho(OakRidgeNationalLaboratory),GeorgeOstruchov(OakRidgeNationalLaboratory),ScottKlasky(OakRidgeNationalLaboratory)

12:00-13:30 Tuesday, June 6, 2017 LunchLocation:PhoenixBallroom

13:30-15:30 Tuesday, June 6, 2017 Research3:SecurityandPrivacyinDistributedSystemsILocation:SalonI

SessionChair:RolandYap(NationalUniversityofSingapore)

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ConsensusRobustnessandTransactionDe-AnonymizationintheRippleCurrencyExchangeSystem AdrianoDiLuzio(SapienzaUniversityofRome),AlessandroMei(SapienzaUniversityofRome),JulindaStefa(SapienzaUniversityofRome)

LearningprivacyhabitsofPDSowners BikashSingh(UniversityofInsubria),BarbaraCarminati(universityofinsubria),ElenaFerrari(universityofinsubria)

City-Hunter:HuntingSmartphonesinUrbanAreas XuefengLiu(HongKongPolytechnicUniversity),JiaqiWen(HongKongPolytechnicUniversity),ShaojieTang(UniversityofTexasatDallas),JiannongCao(HongKongPolytechnicUniversity),JiaxingShen(HongKongPolytechnicUniversity)

WhenSeeingIsn'tBelieving:OnFeasibilityandDetectabilityofScapegoatinginNetworkTomography ShangqingZhao(UniversityofSouthFlorida),ZhuoLu(UniversityofSouthFlorida),CliffWang(NorthCarolinaStateUniversity/ArmyResearchOffice)

YouCanHearButYouCannotSteal:DefendingagainstVoiceImpersonationAttacksonSmartphones SiChen(UniversityatBuffalo/WestChesterUniversity),KuiRen(UniversityatBuffalo),SixuPiao(UniversityatBuffalo),CongWang(CityUniversityofHongKong),QianWang(WuhanUniversity),JianWeng(JinanUniversity),LuSu(UniveristyatBuffalo),AzizMohaisen(UniversityatBuffalo)

FlowReconnaissanceviaTimingAttacksonSDNSwitches ShengLiu(UniversityofNorthCarolinaatChapelHill),MichaelReiter(UniversityofNorthCarolinaatChapelHill),VyasSekar(CarnegieMellonUniversity)

Research4:CloudComputingandDataCenterSystemsLocation:SalonII

SessionChair:VladimirVlassov(KTHRoyalInstituteofTechnology)

AStudyofLong-TailLatencyinn-TierSystems:RPCvs.AsynchronousInvocations QingyangWang(LouisianaStateUniversity),Chien-AnLai(GeorgiaTech),YasuhikoKanemasa(FujitsuLaboratoriesLtd.),ShungengZhang(LouisianaStateUniversity),CaltonPu(GeorgiaTech)

RainorShine?-MakingSenseofCloudyReliabilityData IyswaryaNarayanan(ThePennsylvaniaStateUniversity),BikashSharma(Microsoft),DiWang(Microsoft),SriramGovindan(Microsoft),LauraCaulfield(Microsoft),AnandSivasubramaniam(ThePennsylvaniaStateUniversity),AmanKansal(Microsoft),JieLiu(Microsoft),BadriddineKhessib(Microsoft),KushagraVaid(Microsoft)

Right-sizingGeo-distributedDataCentersforAvailabilityandLatency IyswaryaNarayanan(ThePennsylvaniaStateUniversity),AmanKansal(Microsoft),AnandSivasubramaniam(ThePennsylvaniaStateUniversity)

PerformanceDrivenResourceSharingMarketsfortheSmallCloud Sung-HanLin(UniversityofSouthernCalifornia),RanjanPal(UniversityofSouthernCalifornia),MarcoPaolieri(UniversityofSouthernCalifornia),LeanaGolubchik(UniversityofSouthernCalifornia)

Fault-scalableVirtualizedInfrastructureManagement MukilKesavan(VMwareInc.),AdaGavrilovska(GeorgiaInstituteofTechnology),KarstenSchwan(GeorgiaInstituteofTechnology)

DeltaCFS:BoostingDeltaSyncforCloudStorageServicesbyLearningfromNFS QuanluZhang(PekingUniversity),ZhenhuaLi(TsinghuaUniversity),ZhiYang(PekingUniversity),ShenglongLi(PekingUniversity),YangzeGuo(PekingUniversity),YafeiDai(PekingUniversity),ShouyangLi(PekingUniversity)

Vision2:FutureNetworkingandCyberinfrastructureLocation:Atlanta

SessionChair:ManishParashar(RutgersUniversity)

AVisionforZero-HopNetworking(ZeN) MostafaAmmar(SchoolofComputerScience,GeorgiaTech),EllenZegura(SchoolofComputerScience,GeorgiaTech),YimengZhao(SchoolofComputerScience,GeorgiaTech)

StructuredOverlayNetworksforaNewGenerationofInternetServices AmyBabay(JohnsHopkinsUniversity),ClaudiuDanilov(BoeingResearchandTechnology),JohnLane(LTNGLobalCommunications),MichalMiskin-Amir(LTNGlobalCommunications,SpreadConceptsLLC),DanielObenshain(JohnsHopkinsUniversity),JohnSchultz(LTNGlobalCommunications,SpreadConceptsLLC),JonathanStanton(LTNGlobalCommunications,SpreadConceptsLLC),ThomasTantillo(JohnsHopkinsUniversity),YairAmir(JohnsHopkinsUniversity,LTNGlobalCommunications,SpreadConceptsLLC)

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EnsuringNetworkNeutralityforFutureDistributedSystems ThiagoGarrett(FederalUniversityofParana),SchahramDustdar(TUWien),LuisC.E.Bona(FederalUniversityofParana),EliasP.DuarteJr.(FederalUniversityofParana)

UncoveringtheUsefulStructuresofComplexNetworksinSocially-RichandDynamicEnvironments JieWu(TempleUniversity)

FutureNetworkingChallenges:TheCaseofMobileAugmentedReality TristanBraud(TheHongKongUniversityofScienceandTechnology),FarshidHassaniBijarbooneh(TheHongKongUniversityofScienceandTechnology),DimitrisChatzopoulos(TheHongKongUniversityofScienceandTechnology),PanHui(TheHongKongUniversityofScienceandTechnology)

SoftwareDefinedCyberinfrastructure IanFoster(ArgonneNationalLaboratoryandTheUniversityofChicago),BenBlaiszik(TheUniversityofChicago),KyleChard(ComputationInstitute,UniversityofChicagoandArgonneNationalLab),RyanChard(VictoriaUniversityofWellington)

Application3:InternetofThings,SmartCities,andCyber-PhysicalSystemsLocation:Columbia

SessionChair:GuanhuaYan(BinghamtonUniversity)

OpportunisticEnergySharingBetweenPowerGridandElectricVehicles:AGameTheory-BasedPricingPolicy AnkurSarker(UniversityofVirginia),ZhuozhaoLi(UniversityofVirginia),WilliamKolodzey(ClemsonUniversity),HaiyingShen(UniversityofVirginia)

EnergyEfficientObjectDetectioninCameraSensorNetworks TuanDao(UCRiverside),KarimKhalil(UCRiverside),AmitRoy-Chowdhury(UCRiverside),SrikanthKrishnamurthy(UCRiverside),LanceKaplan(U.S.ArmyResearchLaboratory)

DeepOpp:Context-awareMobileAccesstoSocialMediaContentonUndergroundMetroSystems DiWu(HunanUniversity&ImperialCollegeLondon),DmitriArkhipov(UniversityofCaliforniaIrvine),ThomasPrzepiorka(ImperialCollegeLondon),QiangLiu(DartmouthCollege),JulieMcCann(ImperialCollegeLondon),AmeliaRegan(UniversityofCaliforniaIrvine)

PhaseBeat:ExploitingCSIPhaseDataforVitalSignMonitoringwithCommodityWiFiDevices XuyuWang(AuburnUniversity),ChaoYang(AuburnUniversity),ShiwenMao(AuburnUniversity)

REX:RapidEnsembleClassificationSystemforLandslideDetectionusingSocialMediaAibekMusaev(UniversityofAlabama),DeWang(GeorgiaInstituteofTechnology),JiatengXie(GeorgiaInstituteofTechnology),CaltonPu(GeorgiaInstituteofTechnology)

TowardAnIntegratedApproachtoLocalizingFailuresinCommunityWaterNetworksQingHan(UCIrvine),PhuNguyen(UCIrvine),RonaldT.Eguchi(ImageCat),Kuo-LinHsu(UCIrvine),NaliniVenkatasubramanian(UCIrvine)

Application4:Mobile,Wireless,andEdgeComputingLocation:Nashville

SessionChair:ShiwenMao(AuburnUniversity)

MobiQoR:PushingtheEnvelopeofMobileEdgeComputingviaQuality-of-ResultOptimizationYongboLi(GeorgeWashingtonUniversity),YurongChen(GeorgeWashingtonUniversity),TianLan(GeorgeWashingtonUniversity),GuruVenkataramani(GeorgeWashingtonUniversity)

TruthfulAuctionsforUserDataAllowanceTradinginMobileNetworks ZhongxingMing(TsinghuaUniversity),MingweiXu(TsinghuaUniversity),NingWang(SurreyUniversity),BeijeGao(TsinghuaUniversity),QiLi(TsinghuaUniversity)

OnlineResourceAllocationforArbitraryUserMobilityinDistributedEdgeClouds LinWang(TUDarmstadt),LeiJiao(UniversityofOregon),JunLi(UniversityofOregon),MaxMühlhäuser(TUDarmstadt)

LeveragingTargetk-CoverageinWirelessRechargeableSensorNetworks PengzhanZhou(StonyBrookUniversity),CongWang(StonyBrookUniversity),YuanyuanYang(StonyBrookUniversity)

ReducingCellularSignalingTrafficforHeartbeatMessagesviaEnergy-EfficientD2DForwarding YanqiJin(HuazhongUniversityofScience&Technology),FangmingLiu(HuazhongUniversityofScienceandTechnology),XiaomengYi(HuazhongUniversityofScience&Technology),MinghuaChen(TheChineseUniversityofHongKong)

k-ProtectedRoutingProtocolinMulti-hopCognitiveRadioNetworks

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Chin-JungLiu(MichiganStateUniversity),LiXiao(MichiganStateUniversity)

ShortPaper1:DistributedOperatingSystems,Middleware,andAlgorithmsLocation:Charleston1

SessionChair:SripadNadagowda(IBMTJWatsonResearchCenter)

SRLB:ThePowerofChoicesinLoadBalancingwithSegmentRoutingYoannDesmouceaux(ÉcolePolytechnique),PierrePfister(CiscoSystems),JérômeTollet(CiscoSystems),MarkTownsley(CiscoSystems),ThomasClausen(ÉcolePolytechnique)

ImprovingEfficiencyofLinkClusteringonMulti-CoreMachines GuanhuaYan(BinghamtonUniversity)

S3:JointSchedulingandSourceSelectionforBackgroundTrafficinErasure-CodedStorage ShijingLi(GeorgeWashingtonUniversity),TianLan(GeorgeWashingtonUniversity),Moo-RyongRa(AT&TLabsResearch),RajeshPanta(AT&TLabsResearch)

OntheFeasibilityofInter-domainRoutingviaaSmallBrokerSet DongLin(HuaweiTechnologiesLtdCo.),DavidHui(HuaweiTechnologiesLtdCo.),WeijieWu(HuaweiTechnologiesLtdCo.),TingweiLiu(TheChineseUniversityofHongKong),YatingYang(BeijingInstituteofTechnology),YiWang(TsinghuaUniversity),JohnChi-ShingLui(ChineseUniversityofHongKong),GongZhang(HuaweiTechnologiesLtdCo.),YingtaoLi(HuaweiTechnologiesLtdCo.)

SubscriptionCoveringforRelevance-basedFilteringinContent-BasedPublish/SubscribeSystems KaiwenZhang(TechnischeUniversitatMunchen),VinodMuthusamy(IBMResearch),MohammadSadoghi(PurdueUniversity),Hans-ArnoJacobsen(UniversityofToronto)

WorkflowOptimizationinPAW MaximFilatov(UNIGE),VerenaKantere(UniversityofGeneva)

AFirstLookatInformationEntropy-BasedDataPricing XijunLi(ShanghaiJiaoTongUniversity),JianguoYao(ShanghaiJiaoTongUniversity),XueLiu(McGillUniversity),HaibingGuan(ShanghaiJiaoTongUniversity)

RestrospectiveLightweightDistributedSnapshotsUsingLooselySynchronizedClocks AlekseyCharapko(SUNYBuffalo),AilidaniAilijiang(SUNYBuffalo),MuratDemirbas(SUNYBuffalo),SandeepKulkarni(MichiganStateUniversity)

Power-AwarePopulationProtocols ChuanXu(LRI(CNRS/UPSud)),JannaBurman(LRI(CNRS/UPSud)),JoffroyBeauquier(LRI(CNRS/UPSud))

MultiPub:LatencyandCost-AwareGlobal-ScaleCloudPublish/Subscribe JulienGascon-Samson(McGillUniversity),JörgKienzle(McGillUniversity),BettinaKemme(McGillUniversity)

ReachabilityinBinaryMultithreadedProgramsIsPolynomial AlexanderMalkis(TechnischeUniversitätMünchen),SteffenBorgwardt(UCDavis)

AnEvent-LevelAbstractionforAchievingEfficiencyandFairnessinNetworkUpdate TingQu(NationalUniversityofDefenseTechnology),DekeGuo(NationalUniversityofDefenseTechnology),XiaominZhu(NationalUniversityofDefenseTechnology),JieWu(TempleUniversity),XiaoleiZhou(NationalUniversityofDefenseTechnology),ZhongLiu(NationalUniversityofDefenseTechnology)

15:30-16:00 Tuesday, June 6, 2017 CoffeeBreakLocation:Foyer

16:00-18:00 Tuesday, June 6, 2017 Research5:EdgeandFogComputingLocation:SalonI

SessionChair:WeisongShi(WayneStateUniversity)

Cachier:Edge-cachingforrecognitionapplications UtsavDrolia(CarnegieMellonUniversity),KatherineGuo(BellLabs),JiaqiTan(Nokia),RajeevGandhi(CarnegieMellonUniversity),PriyaNarasimhan(CarnegieMellonUniversity)

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ContentCentricPeerDataSharinginPervasiveEdgeComputingEnvironments XintongSong(PekingUniversity),YaodongHuang(StonyBrookUniversity),QianZhou(StonyBrookUniversity),FanYe(StonyBrookUniversity),YuanyuanYang(StonyBrookUniversity),XiaomingLi(PekingUniversity)

FLARE:CoordinatedRateAdaptationforHTTPAdaptiveStreaminginCellularNetworks YoungbinIm(UniversityofColoradoatBoulder),JinyoungHan(HanyangUniversity),JiHoonLee(JuniKorea),YoonKwon(Kakao),CarleeJoe-Wong(CarnegieMellonUniversity),TaekyoungKwon(SeoulNationalUniversity),SangtaeHa(UniversityofColoradoatBoulder)

NetworkedDroneCamerasforSportsStreaming XiaoliWang(PrincetonUniversity),AakankshaChowdhery(PrincetonUniversity),MungChiang(PrincetonUniversity)

Chronus:ConsistentDataPlaneUpdatesinTimedSDNs JiaqiZheng(NanjingUniversity),GuihaiChen(NanjingUniversity),StefanSchmid(AalborgUniversity),HaipengDai(NanjingUniversity),JieWu(TempleUniversity)

DistributedDeepNeuralNetworksovertheCloud,theEdgeandEndDevicesSuratTeerapittayanon(HarvardUniversity),BradleyMcDanel(HarvardUniversity),H.T.Kung(HarvardUniversity)

Research6:DistributedGreenComputingandEnergyManagementLocation:SalonII

SessionChair:GeraldFLofstead(SandiaNationalLab)

DynamicControlofFlowCompletionTimeForPowerEfficientDataCenterNetworks KuangyuZheng(TheOhioStateUniversity),XiaoruiWang(TheOhioStateUniversity)

OnEnergy-EfficientCongestionControlforMultipathTCP JiaZhao(SimonFraserUniversity),JiangchuanLiu(SimonFraserUniversity),HaiyangWang(UniversityofMinnesotaDuluth)

AMechanismforCooperativeDemand-SideManagement GuangchaoYuan(Microsoft),Chung-WeiHang(IBM),MichaelHuhns(UniversityofSouthCarolina),MunindarSingh(NorthCarolinaStateUniversity)

AHierarchicalFrameworkofCloudResourceAllocationandPowerManagementUsingDeepReinforcementLearning NingLiu(SyracuseUniversity),ZheLi(SyracuseUniversity),ZhiyuanXu(SyracuseUniversity),JielongXu(SyracuseUniversity),ShengLin(SyracuseUniversity),QinruQiu(SyracuseUniversity),JianTang(SyracuseUniversity),YanzhiWang(SyracuseUniversity)

SunChase:Energy-EfficientRoutePlanningforSolar-PoweredEVs. LanduJiang(McGillUniversity),YuHua(HuazhongUniversityofScienceandTechnology),ChenMa(McGillUniversity),XueLiu(McGillUniversity)

Vision3:NextGenerationCloudandEdgeServicesLocation:Atlanta

SessionChair:ManfredHauswirth(TUBerlin)

ComputingintheContinuum:CombiningPervasiveDevicesandServicestoSupportData-drivenApplications ManishParashar(RutgersUniversity),MoustafaAbdelbaky(RutgersUniversity),MengsongZou(RutgersUniversity),AliRezaZamani(RutgersUniversity),EduardRenart(RutgersUniversity),JavierDiaz-Montes(RutgersUniversity)

Decision-drivenExecution:ADistributedResourceManagementParadigmfortheAgeofIoT TarekAbdelzaher(UIUC),TanvirAlAmin(UIUC),AmotzBar-Noy(UIUC),WilliamDron(BBN),RameshGovindan(USC),ReginaldHobbs(ARL),ShaohanHu(IBM),Jung-EunKim(UIUC),ShuochaoYao(UIUC),YiranZhao(UIUC)

ACTiCLOUD:EnablingtheNextGenerationofCloudApplications GeorgiosGoumas(NationalTechnicalUniversityofAthens),KonstantinosNikas(ComputingSystemsLaboratory,NTUA),EwnetuBayuhLakew(Dept.ofComputingScience,UmeaUniversity),ChristosKotselidis(TheUniversityofManchester),VasileiosKarakostas(ComputingSystemsLaboratory,NTUA),AtleVesterkjaer(Numascale),EinarRustad(Numascale),JohnGoodacre(Kaleao),AndrewAttwood(Kaleao),MichailFlouris(OnApp),JohnThomson(OnApp),NikosFoutris(TheUniversityofManchester),MikelLujan(TheUniversityofManchester),YingZhang(MonetDBSolutions),PanagiotisKoutsourakis(MonetDBSolutions),MartinKersten(MonetDBSolutions),JimWebber(NeoTechnology),DavideGrohmann(NeoTechnology),ErikElmroth(Dept.ofComputingScience,UmeaUniversity),LuisTomas(Dept.ofComputingScience,UmeaUniversity),NectariosKoziris(NationalTechnicalUniversityofAthens)

JointCloud:ACross-CloudCooperationArchitectureforIntegratedInternetServiceCustomization HuaiminWang(NationalUniversityofDefenseTechnology),PeichangShi(NationalUniversityofDefenseTechnology),YimingZhang(NationalUniversityofDefenseTechnology)

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SupportingDataAnalyticsApplicationsWhichUtilizeCognitiveServices ArunIyengar(IBMResearch)

TrillionOperationsKey-ValueStorageEngine:RevisitingtheMissionCriticalAnalyticsStorageSoftwareStack SangeethaSeshadri(IBMAlmadenResearchCenter),LawrenceChiu(IBMAlmadenResearchCenter),PaulMuench(IBMAlmadenResearchCenter)

ShortPaper2:CloudandDataCenterSystemsandNetworksLocation:Columbia

SessionChair:QingyangWang(LouisianaStateUniversity)

DCM:DynamicConcurrencyManagementforScalingn-TierApplicationsinCloud HuiChen(LouisianaStateUniversity),QingyangWang(LouisianaStateuniversity),BalajiPalanisamy(UniversityofPittsburgh),PengchengXiong(Hortonworks)

MorePeak,LessDifferentiation:TowardsAPricing-awareOnlineControlFrameworkforInter-DatacenterTransfers WenxinLi(DalianUniversityofTechnology),XiaoboZhou(TiajjinUniversity),KeqiuLi(DalianUniversityofTechnology),HengQi(DalianUniversityofTechnology),DekeGuo(NationalUniversityofDefenceTechnology)

RobustMulti-TenantServerConsolidationintheCloudforDataAnalyticsWorkloads JosephMate(UniversityofWaterloo),KhuzaimaDaudjee(UniversityofWaterloo),ShahinKamali(MITCSAIL)

Flow-AwareAdaptivePacingtoMitigateTCPIncastinDatacenterNetworks ShaojunZou(CentralSouthUniversity),JiaweiHuang(CentralSouthUniversity),YutaoZhou(CentralSouthUniversity),JianxinWang(CentralSouthUniversity),TianHe(UniversityofMinnesota)

Real-TimePowerCyclinginVideoonDemandDataCentresusingOnlineBayesianPrediction VicentSanzMarco(LancasterUniversity),ZhengWang(LancasterUniversity),BarryPorter(LancasterUniversity)

ADistributedAccessControlSystemforCloudFederations ShorouqAlansari(UniversityofSouthampton),FedericaPaci(UniversityofSouthampton),VladimiroSassone(UniversityofSouthampton)

Voyager:CompleteContainerStateMigration ShripadNadgowda(IBMTJWatsonResearchCenter),SahilSuneja(IBMTJWatsonResearchCenter),NiltonBila(IBMTJWatsonResearchCenter),CanturkIsci(IBMTJWatsonResearchCenter)

Keddah:CapturingHadoopNetworkBehaviour JieDeng(QueenMaryUniversityLondon),GarethTyson(QueenMary),FélixCuadrado(QueenMaryUniversityofLondon),SteveUhlig(QueenMaryUniversityofLondon)

AScalableandDistributedApproachforNFVServiceChainCostMinimization ZijunZhang(UniversityofCalgary),ZongpengLi(UniversityofCalgary),ChuanWu(UniversityofHongKong),ChuanheHuang(WuhanUniversity)

ElasticPaxos:ADynamicAtomicMulticastProtocol SamuelBenz(UniversitàdellaSvizzeraitaliana),FernandoPedone(UniversitàdellaSvizzeraitaliana)

BoostingTheBenefitsOfHybridSDN WenWang(McGillUniversity),WenboHe(McMasterUniversity),JinshuSu(NationalUniversityofDefenseTechnology)

AdoptingSDNSwitchBuffer:BenefitsAnalysisandMechanismDesign FuliangLi(NortheasternUniversity),JiannongCao(TheHongKongPolytechnicUniversity),XingweiWang(NortheasternUniversity),YinchuSun(NortheasternUniversity),TianPan(BeijingUniversityofPostsandTelecommunication),XuefengLiu(TheHongKongPolytechnicUniversity)

ShortPaper3:InternetofThings,SmartCities,andCyber-PhysicalSystemsLocation:Nashville

SessionChair:NanLi(CSIRO)

IOTSENTINEL:AutomatedDevice-TypeIdentificationforSecurityEnforcementinIoT MarkusMiettinen(TechnischeUniversitatDarmstadt),SamuelMarchal(AaltoUniversity),IbbadHafeez(UniversityofHelsinki),N.Asokan(AaltoUniversity),Ahmad-RezaSadeghi(TechnischeUniversitatDarmstadt),SasuTarkoma(UniversityofHelsinki)

EfficientZ-orderEncodingBasedMulti-modelDataCompressioninWSNs XiaofeiCao(MissouriUniversityofScienceandTechnology),SanjayMadria(MissouriUniversityofScienceandTechnology),TakahiroHara(OsakaUniversity)

PTrack:EnhancingtheApplicabilityofPedestrianTrackingwithWearables

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YonghangJiang(CityUniversityofHongKong),ZhenjiangLi(CityUniversityofHongKong),JianpingWang(CityUniversityofHongKong)

SourceLocationPrivacy-AwareDataAggregationSchedulingforWirelessSensorNetworks JackKirton(UniversityofWarwick),MatthewBradbury(TheUniversityofWarwick),ArshadJhumka(UniversityofWarwick)

VelocityOptimizationofPureElectricVehicleswithTrafficDynamicsConsideration LiuwangKang(UniversityofVirginia),HaiyingShen(UniversityofVirginia),AnkurSarker(UniversityofVirginia)

PIANO:Proximity-basedUserAuthenticationonVoice-PoweredInternet-of-ThingsDevices NeilZhenqiangGong(IowaStateUniversity),AltayOzen(IowaStateUniversity),YuWu(UCDavis),XiaoyuCao(IowaStateUniversity),RichardShin(UCBerkeley),DawnSong(UCBerkeley),HongxiaJin(SamsungResearchAmerica),XuanBao(GoogleInc.)

CategoryInformationCollectioninRFIDSystems JiaLiu(NanjingUniversity),ShigangChen(UniversityofFlorida),BinXiao(TheHongKongPolytechnicUniversity),YanyanWang(NanjingUniversity),LijunChen(NanjingUniversity)

ScalableRole-basedDataDisclosureControlfortheInternetofThings AliYavari(RMITUniversity),ArezouSoltaniPanah(RMITUniversity),DimitriosGeorgakopoulos(SwinburneUniversityofTechnology),PremPrakashJayaraman(SwinburneUniversityofTechnology),RonvanSchyndel(RMITUniversity)

Multi-representationbasedDataProcessingArchitectureforIoTApplications VaibhavArora(UniversityofCalifornia,SantaBarbara),FaisalNawab(UniversityofCalifornia,SantaBarbara),DivyakantAgrawal(UniversityofCalifornia,SantaBarbara),AmrElAbbadi(UniversityofCalifornia,SantaBarbara)

LongTermSensingviaBatteryHealthAdaptation GregJackson(ImperialCollegeLondon),ZhijinQin(ImperialCollegeLondon),JulieAMcCann(ImperialCollegeLondon)

DetectingTimeSynchronizationAttacksinCyber-PhysicalSystemswithMachineLearningTechniques JingxuanWang(TheUniversityofHongKong),WentingTu(ShanghaiUniversityofFinanceandEconomics),LucasC.K.Hui(TheUniversityofHongKong),SiuMingYiu(TheUniversityofHongKong),EricKeWang(HarbinInstituteofTechnologyShenzhenGraduateSchool)

Speed-basedLocationTrackinginUsage-basedAutomotiveInsurance LuZhou(ShanghaiJiaoTongUniversity),QingrongChen(ShanghaiJiaoTongUniversity),ZutianLuo(ShanghaiJiaoTongUniversity),HaojinZhu(ShanghaiJiaoTongUniversity),CailianChen(ShanghaiJiaoTongUniversity)

Poster1-4Location:Charleston1and2(Lighteningtalksfrom16:00to17:00)

SessionChairs:GeraldFLofstead(SandiaNationalLab)andBalajiPalanisamy(UniversityofPittsburg)

Location:PhoenixBallroom(Displayfrom17:00to19:00)

Poster1:DistributedApplicationsCluster

TowardVehicleSensing:Anintegratedapplicationwithsparsevideocamerasandintelligenttaxicabs YangWang(UniversityofScienceandTechnologyofChina),WujiChen(UniversityofScienceandTechnologyofChina),WeiZheng(Sanofi-AventisUSLLC),HeHuang(SoochowUniversity),WenZhang(UniversityofScienceandTechnologyofChina),HengchangLiu(UniversityofScienceandTechnologyofChina)

SegmentationofTimeSeriesbasedonKineticCharacteristicsforStorageConsumptionPrediction BeibeiMiao(Baidu,Inc),YuChen(Baidu,Inc),XueboJin(SchoolofComputerandInformationEngineering,BeijingTechnologyandBusinessUniversity),BoWang(Baidu,Inc),XianpingQu(Baidu,Inc),DongWang(Baidu,Inc),ShiminTao(Baidu,Inc),ZhiZang(Baidu,Inc)

AMulti-stageHierarchicalWindowModelwithApplicationtoReal-TimeGraphAnalysis SachiniJayasekara(UniversityofMelbourne),ShanikaKarunasekera(UniversityofMelbourne),AaronHarwood(UniversityofMelbourne)

DynamicPricingatElectricVehicleChargingStationsforQueueingDelayReduction XiaoshanSun(UniversityofScienceandTechnologyofChina),PengXu(UniversityofScienceandTechnologyofChina),JinyangLi(UniversityofScienceandTechnologyofChina),HengchangLiu(UniversityofScienceandTechnologyofChina),WeiZheng(Sanofi-Aventis)

PairwiseRankingAggregationbyNon-interactiveCrowdsourcingwithBudgetConstraints ChangjiangCai(StevensInstituteofTechnology),HaipeiSun(StevensInstituteofTechnology),BoxiangDong(MontclairStateUniversity),BoZhang(StevensInstituteofTechnology),TingWang(LehighUniversity),WendyHuiWang(StevensInstituteofTechnology)

Buffer-BasedReinforcementLearningforAdaptiveStreaming YueZhang(SUNYBinghamton),YaoLiu(SUNYBinghamton)

Thecaseforusingcontent-centricnetworkingfordistributinghigh-energyphysicssoftware

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MohammadAlhowaidi(UniversityofNebraska-Lincoln),ByravRamamurthy(UniversityofNebraska-Lincoln),BrianBockelman(UniversityofNebraska-Lincoln),DavidSwanson(UniversityofNebraska-Lincoln)

LAVEA:Latency-awareVideoAnalyticsonEdgeComputingPlatformShanheYi(CollegeofWilliamandMary),ZijiangHao(CollegeofWilliamandMary),QingyangZhang(WayneStateUniversity),QuanZhang(WayneStateUniversity),WeisongShi(WayneStateUniversity),QunLi(CollegeofWilliamandMary)

CompleteToleranceRelationbasedFillingAlgorithmusingSpark JinglingYuan(WuhanUniversityofTechnology),YaoXiang(WuhanUniversityofTechnology),XianZhong(WuhanUniversityofTechnology),MinchengChen(WuhanUniversityofTechnology),TaoLi(UniversityofFlorida)

Poster2:SecurityandPrivacyCluster

TowardsSecurePublicDirectoryforPrivacy-PreservingDataSharingAminFallahi(SyracuseUniversity),XiLiu(SyracuseUniversity),YuzheTang(SyracuseUniversity),ShuangWang(UCSD),RuiZhang(ChineseAcademyofSciences)

AnonymousRoutingtoMaximizeDeliveryRatesinDTNs KazuyaSakai(TokyoMetropolitanUniversity),Min-TeSun(NationalCentralUniversity),Wei-ShinnKu(AuburnUniversity),JieWu(TempleUniversity)

EvaluatingConnectionResiliencefortheOverlayNetworkKademliaHennerHeck(UniversitätKassel),OlgaKieselmann(UniversitätKassel),ArnoWacker(UniversitätKassel)

Shortfall-basedOptimalSecurityProvisioningforInternetofThings AntoninoRullo(UniversityofCalabria),EdoardoSerra(BoiseStateUniversity),JorgeLobo(UniversitatPompeaFabra),ElisaBertino(PurdueUniversity)

GroupDifferentialPrivacy-preservingDisclosureofMulti-levelAssociationGraphs BalajiPalanisamy(UniversityofPittsburgh),ChaoLi(UniversityofPittsburgh),PrashantKrishnamurthy(UniversityofPittsburgh)

TrackingInformationFlowinCyber-PhysicalSystems StefanGries(UniversityofDuisburg-Essen),MarcHesenius(UniversityofDuisburg-Essen),VolkerGruhn(UniversityofDuisburg-Essen)

Privacy-preservingMatchmakinginGeosocialNetworkswithUntrustedServers QiuxiangDong(ArizonaStateUniversity),DijiangHuang(ArizonaStateUniversity)

You’veBeenTricked!AUserStudyoftheEffectivenessofTyposquattingTechniques JeffreySpaulding(SUNYBuffalo),ShambhuUpadhyaya(SUNYBuffalo),AzizMohaisen(SUNYBuffalo)

Real-timeDetectionofIllegalFileTransfersintheCloud AdamBowers(MissouriUniversityofScienceandTechnology),DanLin(MissouriUniversityofScienceandTechnology),AnnaSquicciarini(ThePennsylvaniaStateUniversity),AliHurson(MissouriUniversityofScienceandTechnology

EyesoftheSwarm:Streamers'DetectioninBitTorrent DanielSilva(FluminenseFederalUniversity),AntonioRocha(FluminenseFederalUniversity)

Poster3:CloudsandVirtualizationCluster

Loadpredictionforenergy-awareschedulingforCloudscomputingplatformsAlexandreDambreville(LRI),JoannaTomasik(CentraleSupélec),JohanneCohen(LRI-CNRS),FabienDufoulon(LRI)

Learn-as-you-gowithMegh:EfficientLiveMigrationofVirtualMachines DebabrotaBasu(NationalUniversityofSingapore),XiayangWang(InstituteofParallelandDistributedSystems,ShanghaiJiaoTongUniversity),YangHong(ShanghaiJiaoTongUniversity),HaiboChen(ShanghaiJiaoTongUniversity),StephaneBressan(NationalUniversityofSingapore)

Machine-LearningBasedPerformanceEstimationforDistributedParallelApplicationsinVirtualizedHeterogeneousClusters SeontaeKim(UNIST),NguyenPham(UNIST),WoongkiBaek(UNIST),Young-RiChoi(UNIST)

IncrementalelasticityforNoSQLdatastores AntonisPapaioannou(ICS-FORTHandUniversityofCrete),KostasMagoutis(ICS-FORTHandUniversityofIoannina)

AFrameworkforEfficientEnergySchedulingofSparkWorkloads StathisMaroulis(AthensUniversityofEconomicsandBusiness),NikosZacheilas(AthensUniversityofEconomicsandBusiness),VanaKalogeraki(AthensUniversityofEconomicsandBusiness)

TowardsaCompleteVirtualDataCenterEmbeddingAlgorithmusingHybridStrategy MPGilesh(NationalInstituteofTechnologyCalicut),SDMadhuKumar(NationalInstituteofTechnologyCalicut),LillykuttyJacob(NationalInstituteofTechnologyCalicut),UmeshBellur(IndianInstituteofTechnologyBombay)

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FederatingConsistencyforPartition-ProneNetworks BenjaminBengfort(UniversityofMaryland),PeteKeleher(UniversityofMaryland)

Mitigatingnesting-agnostichypervisorpoliciesinderivativeclouds ChandraPrakash(IITBombay),Prashanth(IITBombay),PurushottamKulkarni(IITBombay),UmeshBellur(IITBombay)

ANovelArchitectureforEfficientFogtoCloudDataManagementinSmartCities AmirSinaeepourfard(UPC),JordiGarcia(UPC),XavierMasip-Bruin(UPC),EvaMarin-Tordera(UPC)

Networklet:ConceptandDeploymentShengZhang(NanjingUniversity),YuLiang(NanjingUniversityofPostsandTelecommunications),ZhuzhongQian(NanjingUniversity),MingjunXiao(UniversityofScienceandTechnologyofChina),JieWu(TempleUniversity),FanyuKong(AntFinancial),SangluLu(NanjingUniversity)

OptimisticCausalConsistencyforGeo-ReplicatedKey-ValueStores KristinaSpirovska(EPFL),DiegoDidona(EPFL),WillyZwaenepoel(EPFL)

AutomatedPerformanceEvaluationforMulti-TierCloudServiceSystemsSubjecttoMixedWorkloads XudongZhao(ShandongUniversity),LizhenCui(ShandongUniversity),JiweiHuang(BeijingUniversityofPostsandTelecommunications),ShijunLiu(ShandongUniversity),LeiLiu(ShandongUniversity),CaltonPu(GeorgiaTech)

DecentralisedRuntimeMonitoringforAccessControlSystemsinCloudFederations MdSadekFerdous(UniversityofSouthampton),AndreaMargheri(UniversityofSouthampton),FedericaPaci(UniversityofSouthampton),MuYang,VladimiroSassone(UniversityofSouthampton)

Poster4:DistributedSystemsandNetworkingCluster

DuoFS:AnAttemptatEnergy-SavingandRetainingReliabilityofStorageSystems ShuYin(HunanUniversity)

AProposalofanEfficientTrafficMatrixEstimationunderPacketDrops KoheiWatabe(NagaokaUniversityofTechnology),ToruMano(NTTNetworkInnovationLaboratories),KimihiroMizutani(NTTNetworkInnovationLaboratories),OsamuAkashi(NTTNetworkInnovationLaboratories),KenjiNakagawa(NagaokaUniversityofTechnology),TakeruInoue(NTTNetworkInnovationLaboratories)

StragglerMitigationforDistributedBehavioralSimulation EmanBinKhunayn(UniversityofMelbourne),ShanikaKarunasekera(UniversityofMelbourne),HairuoXie(UniversityofMelbourne),KotagiriRamamohanarao(UniversityofMelbourne)

SupportingResourceControlforActorSystemsinAkka AhmedAbdelMoamen(UniversityofSaskatchewan),DezhongWang(UniversityofSaskatchewan),NadeemJamali(UniversityofSaskatchewan)

ADistributedOperatingSystemNetworkStackandDeviceDriverforMulticores BMSaifAnsary(ECE,VirginiaTech),AntonioBarbalace(ECE,VirginiaTech),BinoyRavindran(ECE,VirginiaTech),ThomasLazor(ECE,VirginiaTech),Ho-RenChuang(ECE,VirginiaTech)

CachePotentialityofMONs:APrime PeiyanYuan(HenanNormalUniversity),HonghaiWu(HenanUniversityofScienceandTechnology),XiaoyanZhao(HenanNormalUniversity),ZhengnanDong(HenanNormalUniversity)

Oak:User-TargetedWebPerformance MarcelFlores(NorthwesternUniversity),AlexanderWenzel(NorthwesternUniversity),AleksandarKuzmanovic(NorthwesternUniversity)

Ctrl-A:ASelf-*DistributedandIn-bandSDNControlPlaneMarcoCanini(UniversitécatholiquedeLouvain),IosifSalem(ChalmersUniversityofTechnology),LironSchiff(TelAvivUniversity),EladMichaelSchiller(ChalmersUniversityofTechnology),StefanSchmid(AalborgUniversity&TUBerlin)

18:00-20:00 Tuesday, June 6, 2017 ReceptionLocation:PhoenixBallroom

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Day 3 – Wednesday, June 7, 2017

7:30-8:30 Wednesday, June 7, 2017 ContinentalBreakfastLocation:Foyer

8:30-9:30 Wednesday, June 7, 2017 Keynote2byM.TamerÖzsu(UniversityofWaterloo)Location:PhoenixBallroom

SessionChair:KarlAberer(EPFL)

9:30-10:00 Wednesday, June 7, 2017 CoffeeBreakLocation:Foyer

10:00-12:00 Wednesday, June 7, 2017 Research7:InternetofThings,SmartCities,andCyber-PhysicalSystemsLocation:SalonI

SessionChair:JoaoEduardoFerreira(UniversityofSaoPaulo)

PersistentTrafficMeasurementThroughVehicle-to-InfrastructureCommunications HeHuang(SoochowUniversity),Yu-ESun(SoochowUniversity),ShigangChen(UniversityofFlorida),HongliXu(UniversityofScienceandTechnologyofChina),YianZhou(Google)

TagBreathe:MonitorBreathingwithCommodityRFIDSystems YuxiaoHou(TheHongKongPolytechnicUniversity),YanwenWang(TheHongKongPolytechnicUniversity),YuanqingZheng(TheHongKongPolytechnicUniversity)

Double-EdgedSword:IncentivizedVerifiableProductPathQueryforRFID-enabledSupplyChain SaiyuQi(XidianUniversity),YuanqingZheng(TheHongKongPolytechnicUniversity),XiaofengChen(XidianUniversity),JianfengMa(XidianUniversity),YongQi(XianJiaotongUniversity)

TowardsAccurateCorruptionEstimationinZigBeeUnderCross-TechnologyInterference GongLongChen(ZhejiangUniversity),WeiDong(ZhejiangUniversity),ZhiweiZhao(UniversityofElectronicScienceandTechnologyofChina),TaoGu(RMITUniversity)

UnseenActivityRecognition:AHierarchicalActiveTransferLearningApproach MohammadArifUlAlam(UniversityofMarylandBaltimoreCounty),NirmalyaRoy(UniversityofMarylandBaltimoreCounty)

RFIPad:EnablingCost-efficientandDevice-freeIn-airHandwritingusingPassiveTags HanDing(Xi'anJiaotongUniversity),ChenQian(UniversityofCaliforniaSantaCruz),JinsongHan(Xi'anJiaotongUniversity),GeWang(Xi'anJiaotongUniversity),WeiXi(Xi'anJiaotongUniversity),KunZhao(Xi'anJiaotongUniversity),JizhongZhao(Xi'anJiaotongUniversity)

Research8:MobileandWirelessComputingSystemsILocation:SalonII

SessionChair:KarthikSundaresan(NECLaboratoriesAmerica)

RobustIncentiveTreeDesignforMobileCrowdsensing XiangZhang(ArizonaStateUniversity),GuoliangXue(ArizonaStateUniversity),RuozhouYu(ArizonaStateUniversity),DejunYang(ColoradoSchoolofMines),JianTang(SyracuseUniversity)

WearLock:UnlockingYourPhoneviaAcousticsusingSmartwatch ShanheYi(CollegeofWilliamandMary),ZhengruiQin(NorthwestMissouriStateUniversity),NancyCarter(CollegeofWilliamandMary),QunLi(CollegeofWilliamandMary)

ModelingMobileCodeAccelerationintheCloud

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HuberFlores(UniversityofOulu),XiangSu(UniversityofOulu),VassilisKostakos(UniversityofOulu),JukkaRiekki(UniversityofOulu),EemilLagerspetz(UniversityofHelsinki),SasuTarkoma(HelsinkiUniversityofTechnology),PanHui(HKUST),YongLi(TsinghuaUniversity),JukkaManner(AaltoUniversity)

E-Android:ANewEnergyProfilingToolforSmartphones XingGao(CollegeofWIlliamandMary),DachuanLiu(CollegeofWIlliamandMary),DaipingLiu(UniversityofDelaware),HainingWang(UniversityofDelaware),AngelosStavrou(GeorgeMasonUniversity)

LocalandLow-CostWhiteSpaceDetection AhmedSaeed(GeorgiaInstituteofTechnology),KhaledHarras(CarnegieMellonUniversity),EllenZegura(GeorgiaInstituteofTechnology),MostafaAmmar(GeorgiaInstituteofTechnology)

GeneralAnalysisofIncentiveMechanismsforPeer-to-PeerTransmissions:AQuantumGamePerspective WeimanSun(BeijingNormalUniversity),ShenglingWang(BeijingNormalUniversity)

Vision4:SecurityandTrustinFutureSystemsLocation:Atlanta

SessionChair:LingLiu(GeorgiaInstituteofTechnology)

HowComputerScienceRiskstoLoseItsInnocence,andShouldAttempttoTakeResponsibilityKarlAberer(EPFL)

ACognitivePolicyFrameworkforNext-GenerationDistributedFederatedSystems-ConceptsandResearchDirections ElisaBertino(PurdueUniversity),SeraphinCalo(IBM),MarounTouma(IBM),DineshVerma(IBM),ChristopherWilliams(UKDSTL),BrianRivera(ArmyResearchLabs)

MachinetoMachineTrustinSmartCities MargaretLoper(GeorgiaTechResearchInstitute),BrianSwenson(GeorgiaInstituteofTechnology)

LateralThinkingforTrustworthyApps HermannHärtig(TechnischeUniversitätDresden),MichaelRoitzsch(TechnischeUniversitätDresden),CarstenWeinhold(TechnischeUniversitätDresden),AdamLackorzynski(TechnischeUniversitätDresden)

RumorInitiatorDetectioninInfectedSignedNetworks JiaweiZhang(UniversityofIllinoisatChicago),CharuC.Aggarwal(IBMT.J.WatsonResearchCenter),PhilipS.Yu(UniversityofIllinoisatChicago)

AddressingSmartphone-basedMulti-factorAuthenticationviaHardware-rootedTechnologiesZhongjieBa(TheStateUniversityofNewYorkatBuffalo),KuiRen(TheStateUniversityofNewYorkatBuffalo)

Application5:CloudComputingandDataCenterSystemsLocation:Columbia

SessionChair:YuanyuanYang(StonyBrookUniversity)

Multi-ResourceLoadBalancingforVirtualNetworkFunctions TaoWang(HuazhongUniversityofScience&Technology),HongXu(CityUniversityofHongKong),FangmingLiu(HuazhongUniversityofScienceandTechnology)

Learningfromfailureacrossmultipleclusters:Atrace-drivenapproachtounderstanding,predicting,andmitigatingjobterminations NosaybaEl-Sayed(MIT),HongyuZhu(UniversityofToronto),BiancaSchroeder(UniversityofToronto)

RBAY:AScalableandExtensibleInformationPlaneforFederatingDistributedDatacenterResources XinChen(GeorgiaInstituteofTechnology),LitingHu(FloridaInternationalUniversity),DouglasM.Blough(GeorgiaInstituteofTechnology),MichaelA.Kozuch(IntelLabsPittsburgh),MatthewWolf(OakRidgeNationalLaboratory)

Task-awareTCPinDataCenterNetworks SenLiu(CentralSouthUniversity),JiaweiHuang(CentralSouthUniversity),YutaoZhou(CentralSouthUniversity),JianxinWang(CentralSouthUniversity),TianHe(UniversityofMinnesota)

LimitationsofLoadBalancingMechanismsforN-TierSystemsinthePresenceofMillibottlenecks TaoZhu(GeorgiaInstituteofTechnology),JackLi(GeorgiaInstituteofTechnology),JoshKimball(GeorgiaInstituteofTechnology),JunheePark(IndianaUniversity),Chien-AnLai(GeorgiaInstituteofTechnology),CaltonPu(GeorgiaInstituteofTechnology),QingyangWang(LouisianaStateUniversity)

PerformanceAnalysisofCloudComputingCentersServingParallelizableRenderingJobsUsingM/M/c/rQueuingSystems

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XiulinLi(ShandongUniversity),LiPan(ShandongUniversity),JiweiHuang(BeijingUniversityofPostsandTelecommunications),ShijunLiu(ShandongUniversity),YuliangShi(ShandongUniversity),CaltonPu(GeorgiaInstituteofTechnology)

Application6:BigDataSystemsandDistributedDataManagementandAnalyticsLocation:Nashville

SessionChair:ZhengruiQin(NorthwestMissouriStateUniversity)

EvaluationofDeepLearningFrameworksoverDifferentHPCArchitectures ShayanShams(LouisianaStateUniversity),RichardPlatania(LouisianaStateuniversity),KisungLee(LouisianaStateUniversity),Seung-JongPark(LouisianaStateUniversity)

OnAchievingEfficientDataTransferforGraphProcessinginGeo-DistributedDatacenters AmelieChiZhou(InriaRennes),ShadiIbrahim(InriaRennes),BingshengHe(NationalUniversityofSingapore)

GBooster:TowardsAccelerationofGPU-intensiveMobileApplications ElliottWen(VictoriaUniversityofWellington),BryanNg(VictoriaUniversityofWellington),WinstonSeah(VictoriaUniversityofWellington),XueLiu(McGillUniversity),JiannongCao(TheHongKongPolytechnicUniversity),XuefengLiu(HuangzhongUniversityofScienceandTechnology)

Scalingk-NearestNeighborsQueries(Therightway) AtoshumSamuelCahsai(UniversityOfGlasgow),NikosNtarmos(UniversityOfGlasgow),ChristosAnagnostopoulos(UniversityofGlasgow),PeterTriantafillou(UniversityOfGlasgow)

ParallelizingBigDeBruijnGraphConstructiononHeterogeneousProcessors ShuangQiu(TheHongKongUniversityofScienceandTechnology),QiongLuo(TheHongKongUniversityofScienceandTechnology)

Private,yetPractical,MultipartyDeepLearning XinyangZhang(LehighUniversity),ShoulingJi(ZhejiangUniversity),HuiWang(StevensInstituteofTechnology),TingWang(LehighUniversity)

Industry2:MobileComputingandInternetofThingsLocation:Charleston1

SessionChair:RobertBirke(IBMResearchZurich)

OntheLimitsofSubsamplingofLocationTraces MudhakarSrivatsa(IBMT.J.WatsonResearchCenter),RaghuGanti(IBMT.J.WatsonResearchCenter),PrasantMohapatra(UCDavis)

SOM-TC:Self-organizingmapforHierarchicalTrajectoryClustering PranitaDewan(IBMTJWatsonResearchCenter),RaghuGanti(IBMTJWatsonResearchCenter),MudhakarSrivatsa(IBMTJWatsonResearchCenter)

ProcessingEncryptedandCompressedTime-SeriesData MatúšHarvan(EnovosLuxembourgS.A.),SamuelKimoto(OpenSystems),ThomasLocher(ABBCorporateResearch),Yvonne-AnnePignolet(ABBCorporateResearch),JohannesSchneider(UniversityofLiechtenstein)

CalvinConstrained-AFrameworkforIoTApplicationsinHeterogeneousEnvironments AmardeepMehta(UmeåUniversity),RamiBaddour(UniversitádellaSvizzeraitaliana),FredrikSvensson(EricssonResearch),HaraldGustafsson(EricssonResearch),ErikElmroth(UmeåUniversity)

12:00-13:30 Wednesday, June 7, 2017 ConferenceLuncheonLocation:PhoenixBallroom

13:30-15:30 Wednesday, June 7, 2017 Research9:DistributedBigDataSystemsLocation:SalonI

SessionChair:KisungLee(LouisianaStateUniversity)

High-PerformanceandResilientKey-ValueStorewithOnlineErasureCodingforBigDataWorkloads DiptiShankar(TheOhioStateUniversity),XiaoyiLu(TheOhioStateUniversity),DhabaleswarPanda(TheOhioStateUniversity)

ModelingandAnalyzingLatencyintheMemcachedsystemWenxueCheng(TsinghuaUniversity),FengyuanRen(TsinghuaUniversity),WanchunJiang(CentralSouthUniversity),TongZhang(TsinghuaUniversity)

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SpeculativeSlotReservation:EnforcingServiceIsolationforDependentData-ParallelComputations ChenChen(HKUST),WeiWang(HKUST),BoLi(HKUST)

OptimizingShuffleinWide-AreaDataAnalytics ShuhaoLiu(UniversityofToronto),HaoWang(UniversityofToronto),BaochunLi(UniversityofToronto)

JobSchedulingwithoutPriorInformationinBigDataProcessingSystems ZhimingHu(UniversityofToronto),BaochunLi(UniversityofToronto),ZhengQin(InstituteofHighPerformanceComputing),RickSiowMongGoh(InstituteofHighPerformanceComputing)

DistributedLoadBalancinginKey-ValueNetworkedCachesSikderHuq(TheUniversityofIowa),ZubairShafiq(TheUniversityofIowa),SukumarGhosh(TheUniversityofIowa),AmirKhakpour(VerizonDigitalMediaServices),HarkeeratBedi(VerizonDigitalMediaServices)

Research10:DistributedAlgorithmsandTheoryILocation:SalonII

SessionChair:HaiyingShen(UniversityofVirginia)

CognitiveContext-awareDistributedStorageOptimizationinMobileCloudComputing:AStableMatchingbasedApproach DongHan(OaklandUniversity),YeYan(OaklandUniversity),TaoShu(AuburnUniversity),LiuqingYang(ColoradoStateUniversity),ShuguangCui(UniversityofCalifornia,Davis)

FairCachingAlgorithmsforPeerDataSharinginPervasiveEdgeComputingEnvironments YaodongHuang(StonyBrookUniversity),XintongSong(PekingUniversity),FanYe(StonyBrookUniversity),YuanyuanYang(StonyBrookUniversity),XiaomingLi(PekingUniversity)

Latency-DrivenCooperativeTaskComputinginMulti-UserFog-RadioAccessNetworks Ai-ChunPang(NationalTaiwanUniversity),Wei-HoChung(AcademiaSinica),Te-ChuanChiu(NationalTaiwanUniversity),JunshanZhang(ArizonaStateUniversity)

ApproximationandOnlineAlgorithmsforNFV-EnabledMulticastinginSDNs ZichuanXu(UniversityCollegeLondon),WeifaLiang(TheAustralianNationalUniversity),MeitianHuang(TheAustralianNationalUniversity),MikeJia(TheAustralianNationalUniversity),SongGuo(TheHongKongPolytechnicUniversity),AlexGalis(UniversityCollegeLondon)

DistributedAuctionsforTaskAssignmentandSchedulinginMobileCrowdsensingSystems ZhuojunDuan(GeorgiaStateUniversity),WeiLi(GeorgiaStateUniversity),ZhipengCai(GeorgiaStateUniversity)

EffectiveMobileDataTradinginSecondaryAd-hocMarketwithHeterogeneousandDynamicEnvironment HengkySusanto(HuaweiFutureNetworkTheoryLab),HonggangZhang(UniversityofMassachusettsBoston),ShingYipHo(ShareMedia),BenyuanLiu(UniversityofMassachusettsLowell)

Vision5:FutureDistributedSystemsLocation:Atlanta

SessionChair:ManishParashar(RutgersUniversity)

EnablingwideareadataanalyticswithCollaborativeDistributedProcessingPipes(CDPPs) AnjaFeldmann(TUBerlin),ManfredHauswirth(TUBerlin),VolkerMarkl(TUBerlin)

TheMillibottleneckTheoryofPerformanceBugs,andItsExperimentalVerification CaltonPu(GeorgiaInstituteofTechnology),JoshuaKimball(GeorgiaInstituteofTechnology),Chien-AnLai(GeorgiaInstituteofTechnology),TaoZhu(GeorgiaInstituteofTechnology),JackLi,JunheePark,QingyangWang,DeepalJayasinghe,PengchengXiong,SimonMalkowski,QinyiWu,GueyoungJung,YounggyunKoh,GalenSwint

Exacution:EnhancingScientificDataManagementforExascale ScottKlasky(OakRidgeNationalLaboratory),EricSuchyta(OakRidgeNationalLab),MarkAinsworth(BrownUniversity),QingLiu(NewJerseyInstituteofTechnology),BenWhitney(BrownUniversity),MatthewWolf(OakRidgeNationalLaboratory),JongChoi(OakRidgeNationalLaboratory),IanFoster(ArgonneNationalLaboratory),MarkKim(OakRidgeNationalLaboratory),JeremyLogan(UniversityOfTennesseeKnoxville),KshitijMehta(OakRidgeNationalLaboratory),ToddMunson(ArgonneNationalLaboratory),GeorgeOstrouchov(OakRidgeNationalLaboratory),ManishParashar(RutgersUniversity),NorbertPodhorszk(OakRidgeNationalLaboratory),DavidPugmire(OakRidgeNationalLaboratory),LipengWan(OakRidgeNationalLaboratory)

HardwareAccelerationLandscapeforDistributedReal-timeAnalytics:VirtuesandLimitations MohammadrezaNajafi(TechnischeUniversitatMunchen),KaiwenZhang(TechnischeUniversitatMunchen),Hans-ArnoJacobsen(UniversityofToronto),MohammadSadoghi(PurdueUniversity)

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CoordinatingDistributedSpeakingObjects MarcoLippi(DISMI–UniversitàdiModenaeReggioEmilia),MarcoMamei(DISMI–UniversitàdiModenaeReggioEmilia),StefanoMariani(DISMI–UniversitàdiModenaeReggioEmilia),FrancoZambonelli(DISMI–UniversitàdiModenaeReggioEmilia)

Model-DrivenDomain-SpecificMiddleware FabioCosta(FederalUniversityofGoias),KarlMorris(TempleUniversity),FabioKon(UniversityofSãoPaulo),PeterClarke(FloridaInternationalUniversity)

Application7:DistributedMiddlewareSystemsLocation:Columbia

SessionChair:QingyangWang(LouisianaStateUniversity)

FastandFlexibleNetworkingforMessage-orientedMiddleware LarsKroll(KTHRoyalInstituteofTechnology),AlexandruA.Ormenisan(KTHRoyalInstituteofTechnology),JimDowling(KTHRoyalInstituteofTechnology)

TailCut:PowerReductionunderQualityandLatencyConstraintsinDistributedSearchSystems Chih-HsunChou(UniversityofCalifornia,Riverside),LaxmiBhuyan(UniversityofCalifornia,Riverside),ShaoleiRen(UniversityofCalifornia,Riverside)

StoArranger:EnablingEfficientUsageofCloudStorageServicesonMobileDevices YongshuBai(SUNYBinghamton),YifanZhang(SUNYBinghamton)

CharacterizingPerformanceandEnergy-efficiencyofTheRAMCloudStorageSystem YacineTaleb(Inria),ShadiIbrahim(Inria),GabrielAntoniu(Inria),ToniCortes(BarcelonaSupercomputingCenter)

ProactivelySecureCloud-EnabledStorageKarimEldefrawy(HughesResearchLab),TylerKaczmarek(UniversityofCalifornia,Irvine),SkyFaber(UniversityofCalifornia,Irvine)

BEES:Bandwidth-andEnergy-EfficientImageSharingforReal-timeSituationAwareness PengfeiZuo(HuazhongUniversityofScienceandTechnology),YuHua(HuazhongUniversityofScienceandTechnology),XueLiu(McGillUniverisity),DanFeng(HuazhongUniversityofScienceandTechnology),WenXia(HuazhongUniversityofScienceandTechnology),ShundeCao(HuazhongUniversityofScienceandTechnology),JieWu(HuazhongUniversityofScienceandTechnology),YuanyuanSun(HuazhongUniversityofScienceandTechnology),YunchengGuo(HuazhongUniversityofScienceandTechnology)

Application8:DistributedSystemsandOptimizationsLocation:Nashville

SessionChair:AzizMohaisen(StateUniversityofNewYorkatBuffalo)

TransparentFault-ToleranceusingIntra-MachineFull-Software-StackReplication GiulianoLosa(VirginiaTech),AntonioBarbalace(VirginiaTech),YuzhongWen(VirginiaTech),MarinaSadini(VirginiaTech),Ho-RenChuang(VirginiaTech),BinoyRavindran(VirginiaTech)

Apreventiveauto-parallelizationapproachforelasticstreamprocessing RolandKottoKombi(UniversityClaudeBernard),NicolasLumineau(UniversitédeLyon),PhilippeLamarre(INSALyon)

DependableCloudResourceswithGuardian BaraAbusalah(PurdueUniversity),DerekSchatzlein(PurdueUniversity),JulianJamesStephen(PurdueUniversity),MasoudSaeidaArdekani(PurdueUniversity),PatrickEugster(PurdueUniversity)

ACommunication-awareContainerRe-distributionApproachforHighPerformanceVNFs YuchaoZhang(TsinghuaUniversity),YusenLi(NankaiUniversity),KeXu(TsinghuaUniversity),DanWang(HongKongPolytechnicUniversity),MinghuiLi(Baidu),XuanCao(Baidu),QingqingLiang(Baidu)

MinimizingCostinIaaSCloudsviaScheduledInstanceReservation QiushiWang(NanyangTechnologicalUniversity),MingMingTan(NanyangTechnologicalUniversity),XueyanTang(NanyangTechnologicalUniversity),WentongCai(NanyangTechnologicalUniversity)

EfficientDistributedCoordinationatWAN-scale AilidaniAilijiang(SUNYBuffalo),AlekseyCharapko(SUNYBuffalo),MuratDemirbas(SUNYBuffalo),BekirOguzTurkkan(SUNYBuffalo),TevfikKosar(SUNYBuffalo)

ShortPaper4:Mobile,Wireless,Edge,andCrowdComputingLocation:Charleston1

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SessionChair:LakshmishRamaswamy(UniversityofGeorgia)

Onefficientoffloadingcontrolincloudradioaccessnetworkwithmobileedgecomputing TongLi(TsinghuaUniversity),ChathuraSarathchandraMagurawalage(UniversityofEssex),KezhiWang(UniversityofEssex),KeXu(TsinghuaUniversity),KunYang(UniversityofEssex),HaiyangWang(UniversityofMinnesotaatDuluth)

LocationPrivacyinMobileEdgeClouds TingHe(PennsylvaniaStateUniversity),ErtugrulCiftcioglu(ArmyResearchLaboratory),ShiqiangWang(IBM),KevinChan(ArmyResearchLaboratory)

ApproximationDesignforCooperativeRelayDeploymentinWirelessNetworks HaotianWang(ShanghaiJiaoTongUniversity),ShileiTian(ShanghaiJiaoTongUniversity),XiaofengGao(ShanghaiJiaoTongUniversity),LidongWu(UniversityofTexasatTyler),GuihaiChen(ShanghaiJiaoTongUniversity)

DispersingSocialContentinMobileCrowdthroughOpportunisticContacts LeiZhang(SimonFraserUniversity),FengWang(TheUniversityofMississippi),JiangchuanLiu(SouthChinaAgriculturalUniversity)

ALightweightRecommendationFrameworkforMobileUser'sLinkSelectioninDenseNetwork JiWang(NationalUniversityofDefenseTechnology),XiaominZhu(NationalUniversityofDefenseTechnology),WeidongBao(NationalUniversityofDefenseTechnology),GuanlinWu(NationalUniversityofDefenseTechnology)

MakingSmartphoneSmartonDemandforLongerBatteryLife MarcoBrocanelli(TheOhioStateUniversity),XiaoruiWang(TheOhioStateUniversity)

FADEWICH:FastDeauthenticationovertheWirelessChannel GiulioLovisotto(UniversityofOxford),MauroConti(UniversityofPadua),IvanMartinovic(UniversityofOxford),GeneTsudik(UniversityofCalifornia,Irvine)

CognitiveWirelessCharger:Sensing-BasedReal-TimeFrequencyControlForNear-FieldWirelessCharging Sang-YoonChang(UniversityofColoradoColoradoSprings),SristiLakshmiSravanaKumar(AdvancedDigitalSciencesCenter),Yih-ChunHu(UniversityofIllinoisatUrbana-Champaign)

DensityandMobility-drivenEvaluationofBroadcastAlgorithmsforMANETs RazielCarvajalGómez(UniversityofNeuchatel),IntiGonzalez-Herrera(LaBRI/UniversityofBordeaux),Yérom-DavidBromberg(UniversityofRennes1),LaurentRéveillère(UniversityofBordeaux),EtienneRivière(UniversityofNeuchatel)

Energy-AwareCPUFrequencyScalingforMobileVideoStreaming WenjieHu(ThePennsylvaniaStateUniversity),GuohongCao(ThePennsylvaniaStateUniversity)

CrazyCrowdSourcingtoMitigateResourceScarcity NovaAhmed(NorthSouthUniversity),MdMahfuzurRahmanSiddiquee(IndependentResearcher),RefayaKarim(NorthSouthUniversity),MohsinaZaman(IndependentResearcher),SayedMahmudulAlam(NorthSouthUniversity),SyedFahimAsraf(NorthSouthUniversity)

DetectingRogueAPwiththeCrowdWisdom TongqingZhou(NationalUniversityofDefenseTechnology),ZhipingCai(NationalUniversityofDefenseTechnology),BinXiao(TheHongKongPolytechnicUniversity),YueyueChen(NationalUniversityofDefenseTechnology),MingXu(NationalUniversityofDefenseTechnology)

15:30-16:00 Wednesday, June 7, 2017 CoffeeBreakLocation:Foyer

16:00-18:00 Wednesday, June 7, 2017 Research11:SecurityandPrivacyinDistributedSystemsIILocation:SalonI

SessionChair:KuiRen(StateUniversityofNewYorkatBuffalo)

Kalis-ASystemforKnowledge-drivenAdaptableIntrusionDetectionfortheInternetofThings DanieleMidi(PurdueUniversity),AntoninoRullo(UniversityofCalabria),AnandMudgerikar(PurdueUniversity),ElisaBertino(PurdueUniversity)

FuzzyExtractorsforBiometricIdentification NanLi(CSIRO),FuchunGuo(UniversityofWollongong),YiMu(UniversityofWollongong),WillySusilo(UniversityofWollongong),SuryaNepal(CSIRO)

SmartphonePrivacyLeakageofSocialRelationshipsandDemographicsfromSurroundingAccessPoints

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ChenWang(StevensInstituteofTechnology),ChuyuWang(StevensInstituteofTechnology),YingyingChen(StevensInstituteofTechnology),LeiXie(NanjingUniversity),SangluLu(NanjingUniversity)

EV-Matching:BridgingLargeVisualDataandElectronicDataforEfficientSurveillanceGangLi(TheOhioStateUniversity),FanYang(TheOhioStateUniversity),GuoxingChen(TheOhioStateUniversity),QiangZhai(TheOhioStateUniversity),XinfengLi(TheOhioStateUniversity),JinTeng(TheOhioStateUniversity),JundaZhu(UniversityofMacau),DongXuan(TheOhioStateUniversity),BiaoChen(UniversityofMacau),WeiZhao(UniversityofMacau)

AdaptiveReconnaissanceAttackswithNear-OptimalParallelBatching XiangLi(UniversityofFlorida),JohnathanSmith(UniversityofFlorida),MyThai(UniversityofFlorida)

AchievingStrongPrivacyinOnlineSurvey YouZhou(UniversityofFlorida),YianZhou(UniversityofFlorida),ShigangChen(UniversityofFlorida),SamuelS.Wu(UniversityofFlorida)

Research12:CloudComputingandDistributedDataAnalyticsLocation:SalonII

SessionChair:KekeChen(WrightStateUniversity)

ServiceOverlayForestEmbeddingforSoftware-DefinedCloudNetworks Jian-JhihKuo(AcademiaSinica),Shan-HsiangShen(NationalTaiwanUniversityofScienceandTechnology),Ming-HongYang(UniversityofMinnesota),De-NianYang(AcademiaSinica),Ming-JerTsai(NationalTsingHuaUniversity),Wen-TsuenChen(AcademiaSinica)

JointOptimizationofChainPlacementandRequestSchedulingforNetworkFunctionVirtualization QixiaZhang(HuazhongUniversityofScience&Technology),YikaiXiao(HuazhongUniversityofScience&Technology),FangmingLiu(HuazhongUniversityofScienceandTechnology),JohnChiShingLui(ChineseUniversityofHongKong),JianGuo(HuazhongUniversityofScience&Technology),TaoWang(HuazhongUniversityofScience&Technology)

BIGCacheAbstractionforCacheNetworks EmanRamadan(UniversityofMinnesota),ArvindNarayanan(UniversityofMinnesota),Zhi-LiZhang(UniversityofMinnesota),RunhuiLi(HuaweiFutureNetworkTheoryLab),GongZhang(HuaweiFutureNetworkTheoryLab)

DistributedQRdecompositionframeworkfortrainingSupportVectorMachines JyotikrishnaDass(TexasA&MUniversity),V.N.S.PrithviSakuru(TexasA&MUniversity),VivekSarin(TexasA&MUniversity),RabiN.Mahapatra(TexasA&MUniversity)

DistributivelyComputingRandomWalkBetweennessCentralityinLinearTime Qiang-ShengHua(HuazhongUniversityofScienceandTechnology),MingAi(HuazhongUniversityofScienceandTechnology),HaiJin(HuazhongUniversityofScienceandTechnology),DongxiaoYu(HuazhongUniversityofScienceandTechnology),XuanhuaShi(HuazhongUniversityofScienceandTechnology)

DeGPar:LargeScaleTopicDetectionusingNode-CutPartitioningonDenseWeightedGraphs KambizGhoorchian(RoyalInstituteofTechnology(KTH)),SarunasGirdzijauskas(RoyalInstituteofTechnology(KTH)),FatemehRahimian(SwedishInstituteofComputerScience(SICS))

Vision6:InnovationinBigDataSystemsLocation:Atlanta

SessionChair:ManfredHauswirth(TUBerlin)

OntheDesignofaBlockchainPlatformforClinicalTrialandPrecisionMedicine ZonyinShae(ASIAUniversity,Taiwan),JeffreyTsai(ASIAUniversity,Taiwan)

TowardsDataflow-basedGraphAccelerator HaiJin(HuazhongUniversityofScienceandTechnology),PengchengYao(HuazhongUniversityofScienceandTechnology),XiaofeiLiao(HuazhongUniversityofScienceandTechnology),LongZheng(HuazhongUniversityofScienceandTechnology),XianliangLi(HuazhongUniversityofScienceandTechnology)

TowardsaRISCFrameworkforEfficientContextualizationinIoT DimitriosGeorgakopoulos(SwinburneUniversity),AliYavari(RMITUniversity),PremPrakashJayaraman(SwinburneUniversity),RajivRanjan(NewcastleUniversity)

TheFutureoftheSemanticWeb:PrototypesonaGlobalDistributedFilesystem MichaelCochez(Fraunhofer-FIT),DominikHüser(RWTHAachenUniversity),StefanDecker(RWTHAachen)

OnBroadBigData SteffenStaab(InstitutWeST,UniversityKoblenz-LandauandWAIS,UniversityofSouthampton)

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Research13:DistributedAlgorithmsandTheoryIILocation:SalonII

SessionChair:AzizMohaisen(StateUniversityofNewYorkatBuffalo)

NetworkedStochasticMulti-ArmedBanditswithCombinatorialStrategies ShaojieTang(UniversityofTexasatDallas),YaqinZhou(SUTD),KaiHan(UniversityofScienceandTechnologyofChina),ZhaoZhang(ZhejiangNormalUniversity),JingYuan(UniversityofTexasatDallas),WeiliWu(UniversityofTexasatDallas)

ComputabilityofPerpetualExplorationinHighlyDynamicRings MarjorieBournat(UPMCSorbonneUniversités),SwanDubois(UPMCSorbonneUniversités),FranckPetit(UPMCSorbonneUniversités)

LocallySelf-AdjustingSkipGraphs SikderHuq(TheUniversityofIowa),SukumarGhosh(TheUniversityofIowa)

OnlinetoOfflineBusiness:UrbanTaxiDispatchingwithPassenger-DriverMatchingStability HuanyangZheng(TempleUniversity),JieWu(TempleUniversity)

AnOptimizationFrameworkForOnlineRide-sharingMarkets YongzhengJia(TsinghuaUniversity),WeiXu(TsinghuaUniversity),XueLiu(McGillUniversity)

FastandAccurateTrackingofPopulationDynamicsinRFIDSystems MuhammadShahzad(NorthCarolinaStateUniversity),AlexLiu(MichiganStateUniversity)

ShortPaper5:DistributedBigDataSystemsandAnalyticsLocation:Nashville

SessionChair:VarunSoundararajan(Google)

TowardsMultilingualAutomatedClassificationSystems AibekMusaev(UniversityofAlabama),CaltonPu(GeorgiaInstituteofTechnology)

TheJointEffectsofTweetContentSimilarityandTweetInteractionsforTopicDerivation RobertusNugroho(MacquarieUniversity),WeiliangZhao(MacquarieUniversity),JianYang(MacquarieUniversity),CecileParis(CSIRO–ICTCentre),SuryaNepal(CSIRO)

Timed-releaseofSelf-emergingDatausingDistributedHashTables ChaoLi(UniversityofPittsburgh),BalajiPalanisamy(UniversityofPittsburgh)

CachingforPatternMatchingQueriesinTimeEvolvingGraphs:ChallengesandApproaches MuhammadNisar(UniversityofGeorgia),SaharVoghoei(UniversityofGeorgia),LakshmishRamaswamy(UniversityofGeorgia)

GraphA:AdaptivePartitioningforNaturalGraphs DongshengLi(NationalUniversityofDefenseTechnology),ChengfeiZhang(NationalUniversityofDefenseTechnology),JinyanWang(NationalUniversityofDefenseTechnology),ZhaoningZhang(NationalUniversityofDefenseTechnology),YimingZhang(NationalUniversityofDefenseTechnology)

ParallelAlgorithmforCoreMaintenanceinDynamicGraphs NaWang(HuazhongUniversityofScienceandTechnology),DongxiaoYu(HuazhongUniversityofScienceandTechnology),HaiJin(HuazhongUniversityofScienceandTechnology),ChenQian(HuazhongUniversityofScienceandTechnology),XiaXie(HuazhongUniversityofScienceandTechnology),Qiang-ShengHua(HuazhongUniversityofScienceandTechnology)

DHCRF:ADistributedConditionalRandomFieldAlgorithmonHeterogeneousCPU-GPUClusterforBigData AiWei(HunanUniversity),LiKenli(HunanUniversity),ChenCen(HunanUniversity),PengJiwu(HunanUniversity),LiKeqin(HunanUniversity)

TowardsNewAbstractionsforImplementingQuorum-basedSystems TormodErevikLea(UniversityofStavanger),LeanderJehl(UniversityofStavanger),HeinMeling(UniversityofStavanger)

SelectiveTrafficOffloadingOntheFly:aMachineLearningApproach ZaiyangTang(HuazhongUniversityofScienceandTechnology),PengLi(TheUniversityofAizu),SongGuo(TheHongKongPolytechnicUniversity),XiaofeiLiao(HuazhongUniversityofScienceandTechnology),HaiJin(HuazhongUniversityofScienceandTechnology),DaqingZhang(InstitutMines-Telecom,TelecomSudParis)

AFastHeuristicAttributeReductionAlgorithmusingSpark MinchengChen(WuhanUniversityofTechnology),JinglingYuan(WuhanUniversityofTechnology),LinLi(WuhanUniversityofTechnology),DonglingLiu(WuhanUniversityofTechnology),TaoLi(UniversityofFlorida)

ProfilingUsersbyModelingWebTransactions

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RadekTomsu(AaltoUniversity),SamuelMarchal(AaltoUniversity),N.Asokan(AaltoUniversity)

JeCache:Just-EnoughDataCachingwithJust-in-TimePrefetchingforBigDataApplications YifengLuo(FudanUniversity),JiaShi(FudanUniversity),ShuigengZhou(FudanUniversity)

Demo1-4Location:Charleston1and2

SessionChairs:GeraldFLofstead(SandiaNationalLab)andBalajiPalanisamy(UniversityofPittsburg)

Demo1:DistributedApplicationsCluster

LITMUS:TowardsMultilingualReportingofLandslides AibekMusaev(UniversityofAlabama),QixuanHou(GeorgiaInstituteofTechnology),YangYang(GeorgiaInstituteofTechnology),CaltonPu(GeorgiaInstituteofTechnology)

Pythia:ASystemforOnlineTopicDiscoveryofSocialMediaPosts IoulianaLitou(AthensUniversityofEconomicsandBusiness),VanaKalogeraki(AthensUniversityofEconomicsandBusiness)

Data-drivenSerendipityNavigationinUrbanPlaces XiaoyuGe(UniversityofPittsburgh),AmeyaDaphalapurkar(UniversityofPittsburgh),ManaliShimpi(UniversityofPittsburgh),KohliDarpun(UniversityofPittsburgh),KonstantinosPelechrinis(UniversityofPittsburgh),PanosChrysanthis(UniversityofPittsburgh),DemetriosZeinalipour-Yazti(MaxPlanckInstituteforInformaticsandUniversityofCyprus)

TowardAnIntegratedApproachtoLocalizingFailuresinCommunityWaterNetworks(DEMO) QingHan(UniversityofCaliforniaIrvine),PhuNguyen(UniversityofCaliforniaIrvine),RonaldT.Eguchi(ImageCat,Inc.),Kuo-LinHsu(UniversityofCaliforniaIrvine),NaliniVenkatasubramanian(UniversityofCaliforniaIrvine)

Demo2:SecurityandPrivacyCluster

PrivateGraph:ACloud-CentricSystemforPrivacy-PreservingSpectralAnalysisofLargeEncryptedGraphs SagarSharma(WrightStateUniversity),KekeChen(WrightStateUniversity)

IoTSentinelDemo:AutomatedDevice-TypeIdentificationforSecurityEnforcementinIoT MarkusMiettinen(TechnischeUniversitatDarmstadt),SamuelMarchal(AaltoUniversity),IbbadHafeez(universityofhelsinki),TommasoFrassetto(TechnischeUniversitatDarmstadt),N.Asokan(AaltoUniversity),Ahmad-RezaSadeghi(TechnischeUniversitatDarmstadt),SasuTarkoma(UniversityofHelsinki)

RogueAccessPointDetectorUsingCharacteristicsofChannelOverlappingin802.11nRhonghoJang(INHAuniversityofKorea),JeonilKang(INHAuniversity),AzizMohaisen(SUNYBuffalo),DaehunNyang(DepartmentofComputerandInformationEngineering,INHAUniversity,Incheon,Korea)

ReverseCloak:AReversibleMulti-levelLocationPrivacyProtectionSystem ChaoLi(UniversityofPittsburgh),BalajiPalanisamy(UniversityofPittsburgh),AravindKalaivanan(UniversityofPittsburgh),SriramRaghunathan(UniversityofPittsburgh)

Demo3:CloudsandVirtualizationCluster

Hopsworks:ImprovingUserExperienceandDevelopmentonHadoopwithScalable,StronglyConsistentMetadata MahmoudIsmail(KTH-RoyalInstituteofTechnology),ErmiasGebremeskel(RISESICS),TheofilosKakantousis(RISESICS),GautierBerthou(RISESICS),JimDowling(KTH-RoyalInstituteofTechnology)

IsolationinDockerthroughLayerEncryption IoannisGiannakopoulos(NationalTechnicalUniversityofAthens),KonstantinosPapazafeiropoulos(NationalTechnicalUniversityofAthens),KaterinaDoka(NationalTechnicalUniversityofAthens),NectariosKoziris(NationalTechnicalUniversityofAthens)

Dela-SharingLargeDatasetsbetweenHadoopClusters AlexandruA.Ormenisan(KTHRoyalInstituteofTechnology),JimDowling(KTHRoyalInstituteofTechnology)

InVivoEvaluationoftheSecureOpportunisticSchemesMiddlewareusingaDelayTolerantSocialNetworkCoreyE.Baker(UniversityofCaliforniaSanDiego),AllenStarke(UniversityofFlorida),TanishaG.Hill-Jarrett(UniversityofFlorida),JaniseMcNair(UniversityofFlorida)

Demo4:DistributedSystemsandNetworkingCluster

ScalingandLoadTestingLocation-basedPublishandSubscribe BertilChapuis(UniversityofLausanne),BenoîtGarbinato(UniversityofLausanne)

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ADistributedEvent-centricCollaborativeWorkflowsDevelopmentSystemforIoTApplication YongyangCheng(BUPT),ShuaiZhao(BUPT),BoCheng(BUPT),ShouluHou(BUPT),XiuleiZhang(BUPT),JunliangChen(BUPT)

IncentiveMechanismforData-CentricMessageDeliveryinDelayTolerantNetworks HimanshuJethawa(MissouriUniversityofScienceandTechnology),SanjayMadria(MissouriUniversityofScienceandTechnology)

PerformanceOfCognitiveWirelessChargerForNear-FieldWirelessChargingSang-YoonChang(UniversityofColorado,ColoradoSpringsandAdvancedDigitalSciencesCenter),SristiLakshmiSravanaKumar(AdvancedDigitalSciencesCenter),Yih-ChunHu(UniversityofIllinoisatUrbana-Champaign)

19:00-21:00 Wednesday, June 7, 2017 OrganizationEvent(InvitationOnly)

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Day 4 – Thursday, June 8, 2017

7:30-8:30 Thursday, June 8, 2017 ContinentalBreakfastLocation:Foyer

8:30-9:30 Thursday, June 8, 2017 Keynote3byKennethCalvert(NationalScienceFoundation)Location:PhoenixBallroom

SessionChair:CaltonPu(GeorgiaInstituteofTechnology)

9:30-10:00 Thursday, June 8, 2017 CoffeeBreakLocation:Foyer

10:00-12:00 Thursday, June 8, 2017 Tutorial2:SensorCloud:ACloudofSensorNetworksLocation:Charleston1

SanjayMadria(MissouriUniversityofScienceandTechnology)

Research14:MobileandWirelessComputingSystemsIILocation:SalonI

SessionChair:CarleeJoe-Wong(CarnegieMellonUniversity)

RobustIndoorWirelessLocalizationUsingSparseRecovery WeiGong(SimonFraserUniversity),JiangchuanLiu(SimonFraserUniversity)

Max-MinFairResourceAllocationinHetNets:DistributedAlgorithmsandHybridArchitecture EhsanAryafar(PortlandStateUniversity),AlirezaKeshavarz-Haddad(ShirazUniversity),CarleeJoe-Wong(CarnegieMellonUniversity),MungChiang(PrincetonUniversity)

OptimizationofFull-ViewBarrierCoveragewithRotatableCameraSensors XiaofengGao(ShanghaiJiaoTongUniversity),RuiYang(UniversityofIllinoisUrbana-Champaign),FanWu(ShanghaiJiaoTongUniversity),GuihaiChen(ShanghaiJiaoTongUniversity),JingguangZhou(ShanghaiJiaoTongUniversity)

CommunicationthroughSymbolSilence:TowardsFreeControlMessagesinIndoorWLANs BingFeng(UniversityofScienceandTechnologyofChina),JianqingLiu(UniversityofFlorida),ChiZhang(UniversityofScienceandTechnologyofChina),YuguangFang(UniversityofFlorida)

Secureconnectivityofwirelesssensornetworksunderkeypredistributionwithon/offchannels JunZhao(CarnegieMellonUniversity)

iUpdater:LowCostRSSFingerprintsUpdatingforDevice-freeLocalization LiqiongChang(NorthwestUniversity),JieXiong(SingaporeManagementUniversity),YuWang(UniversityofNorthCarolinaatCharlotte),XiaojiangChen(NorthwestUniversity),JunhaoHu(NorthwestUniversity),FangDingyi(NorthwestUniversity)

Research15:SocialNetworksandCrowdsourcingLocation:Columbia

SessionChair:TingWang(LehighUniversity)

InfluenceMaximizationinaManyCascadesWorldIoulianaLitou(AUEB),VanaKalogeraki(AUEB),DimitriosGunopulos(UoA)

Expertise-AwareTruthAnalysisandTaskAllocationinMobileCrowdsourcingXiaomeiZhang(UniversityofSouthCarolinaBeaufort),YiboWu(PennsylvaniaStateUniversity),LifuHuang(RensselaerPolytechnicInstitute),HengJi(RensselaerPolytechnicInstitute),GuohongCao(PennsylvaniaStateUniversity)

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MeLoDy:ALong-termDynamicQuality-awareIncentiveMechanismforCrowdsourcing HongweiWang(ShanghaiJiaoTongUniversity),SongGuo(TheHongKongPolytechnicUniversity),JiannongCao(TheHongKongPolytechnicUniversity),MinyiGuo(ShanghaiJiaoTongUniversity)

TheStrongLinkGraphforEnhancingSybilDefenses SuhendryEffendy(NationalUniversityofSingapore),RolandYap(NationalUniversityofSingapore)

MechanismDesignforMobileCrowdsensingwithExecutionUncertainty ZhenzheZheng(ShanghaiJiaoTongUniversity),ZhaoxiongYang(ShanghaiJiaoTongUniversity),FanWu(ShanghaiJiaoTongUniversity),GuihaiChen(ShanghaiJiaoTongUniversity)

TowardsScalableandDynamicSocialSensingUsingADistributedComputingFramework DanielZhang(UniversityofNotreDame),ChaoZheng(UniversityofNotreDame),DongWang(UniversityofNotreDame),DougThain(UniversityofNotreDame),XinMu(UniversityofNotreDame),GregMadey(UniversityofNotreDame),ChaoHuang(UniversityofNotreDame)

Application9:DistributedSystemsandApplicationsLocation:Atlanta

SessionChair:XueyanTang(NanyangTechnologicalUniversity)

SpecifyingaDistributedSnapshotAlgorithmasaMeta-programandModelCheckingitatMeta-level HaThiThuDoan(JapanAdvancedInstituteofScienceandTechnology),FrancoisBonnet(OsakaUniversity),KazuhiroOgata(JapanAdvancedInstituteofScienceandTechnology)

Self-EvolvingSubscriptionsforContent-BasedPublish/SubscribeSystems CesarCañas(McGillUniversity),KaiwenZhang(TechnicalUniversityofMunich),BettinaKemme(McGillUniversity),JörgKienzle(McGillUniversity),Hans-ArnoJacobsen(TechnicalUniversityofMunich)

ScalableRoutingforTopic-basedPublish/SubscribeSystemsunderFluctuations VolkerTurau(HamburgUniversityofTechnology),GerrySiegemund(HamburgUniversityofTechnology)

OPPay:DesignandImplementationofAPaymentSystemforOpportunisticDataServices FengruiShi(ImperialCollegeLondon),ZhijinQin(ImperialCollegeLondon),JulieMcCann(ImperialCollegeLondon)

OptimalResourceAllocationforMulti-userVideoStreamingovermmWaveNetworksZhifengHe(AuburnUniversity),ShiwenMao(AuburnUniversity)

AMulti-AgentParallelApporachtoAnalyzingLargeClimateDataSets JasonWoodring(UniversityofWashingtonBothell),MatthewSell(UniversityofWashingtonBothell),MunehiroFukuda(UniversityofWashingtonBothell),HazelineAsuncion(UniversityofWashingtonBothell),EricSalathe(UniversityofWashingtonBothell)

Application10:DistributedSystemsandServicesLocation:Columbia

SessionChair:SangeethaSeshadri(IBMAlmadenResearchCenter)

EnergyProportionalServers:WhereAreWein2016? CongfengJiang(HangzhouDianziUniversity),YumeiWang(HangzhouDianziUniversity),DongyangOu(HangzhouDianziUniversity),BingLuo(WayneStateUniversity),WeisongShi(WayneStateUniversity)

AreHTTP/2ServersReadyYet? MuhuiJiang(TheHongKongPolytechnicUniversity),XiapuLuo(TheHongKongPolytechnicUniversity),TungngaiMiu(NexusguardLimited),ShengtuoHu(TheHongKongPolytechnicUniversity),WeixiongRao(TongjiUniversity)

DataIntegrityforCollaborativeApplicationsoverHostedServices ErtemEsiner(NanyangTechnologicalUniversity),AnwitamanDatta(NanyangTechnologicalUniversity)

VirtualMachinePowerAccountingwithShapleyValue WeixiangJiang(HuazhongUniversityofScience&Technology),FangmingLiu(HuazhongUniversityofScienceandTechnology),GuomingTang(UniversityofVictoria),KuiWu(UniversityofVictoria),HaiJin(HuazhongUniversityofScience&Technology)

AVersatilePlatformforMobileDataGatheringExperimentsinWirelessSensorNetworks JiLi(StonyBrookUniversity),CongWang(StonyBrookUniversity),YuanyuanYang(StonyBrookUniversity)

OnDirectionalNeighborDiscoveryinmmWaveNetworks YuWang(AuburnUniversity),ShiwenMao(AuburnUniversity),TheodoreS.Rappaport(NewYorkUniversity)

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ShortPaper6:Security,Privacy,Trust,andFaultToleranceinDistributedSystemsLocation:Nashville

SessionChair:BalajiPalanisamy(UniversityofPittsburgh)

ProximityAwarenessApproachtoEnhancePropagationDelayontheBitcoinPeer-to-PeerNetwork MuntadherFadhilSallal(UniversityofPortsmouth),GarethOwenson(UniversityofPortsmouth),MoAdda(UniversityofPortsmouth)

CatchMeIfYouCan:DetectingCompromisedUsersThroughPartialObservationonNetworksDerekWang(DeakinUniversity),ShengWen(DeakinUniversity),JunZhang(DeakinUniversity),SuryaNepal(Data61),YangXiang(DeakinUniversity),WanleiZhou(DeakinUniversity)

LocationPrivacyBreach:AppsAreWatchingYouinBackground DachuanLiu(CollegeofWilliam&Mary),XingGao(CollegeofWilliam&Mary),HainingWang(UniversityofDelaware)

AndroidMalwareDetectionusingComplex-Flows FengShen(SUNYBuffalo),JustinDelVecchio(SUNYBuffalo),AzizMohaisen(SUNYBuffalo),StevenY.Ko(SUNYBuffalo),LukaszZiarek(SUNYBuffalo)

PrivacyImplicationsofDNSSECLook-asideValidation AzizMohaisen(SUNYBuffalo),ZhongshuGu(IBMResearch),KuiRen(SUNYBuffalo)

FlipNet:ModelingCovertandPersistentAttacksonNetworkedResources SudipSaha(VirginiaPolytechnicInstituteandStateUniversity),AnilVullikanti(VirginiaPolytechnicInstituteandStateUniversity),MahanteshHalappanavar(PacificNorthwestNationalLab)

UnderstandingtheMarket-levelandNetwork-levelBehaviorsoftheAndroidMalwareEcosystem ChaoYang(Niara,Inc.),JialongZhang(IBMResearch),GuofeiGu(TexasA&MUniversity)

EnGarde:Mutually-TrustedInspectionofSGXEnclaves. HaiNguyen(RutgersUniversity),VinodGanapathy(RutgersUniversity)

TruthfulOnlineAuctionforCloudInstanceSubletting YifeiZhu(SimonFraserUniversity),SilveryFu(SimonFraserUniversity),JiangchuanLiu(SimonFraserUniversity),YongCui(TsinghuaUniversity)

OnthePowerofWeakerPairwiseInteraction:Fault-TolerantSimulationofPopulationProtocols GiuseppeAntonioDiLuna(LaSapienza),PaolaFlocchini(UniversityofOttawa),TaisukeIzumi(NagoyaInstituteofTechnology),TomokoIzumi(CollegeofInformationScienceandEngineering),NicolaSantoro(CarletonUniversity),GiovanniViglietta(UniversityofOttawa)

DistributedFaultTolerantLinearSystemSolversbasedonErasureCoding XuejiaoKang(PurdueUniversity--WestLafayette),DavidF.Gleich(PurdueUniversity--WestLafayette),AhmedSameh(PurdueUniversity--WestLafayette),AnanthGrama(PurdueUniversity--WestLafayette)

PreservingIncumbentUsers’PrivacyinExclusion-Zone-BasedSpectrumAccessSystems YanzhiDou(VirginiaTech),HeLi(VirginiaTech),KexiongZeng(VirginiaTech),JinshanLiu(VirginiaTech),YalingYang(VirginiaTech),BoGao(ChineseAcademyofSciences),KuiRen(SUNYBuffalo)

12:00-13:30 Thursday, June 8, 2017 BusinessLunchincludingAwardsandICDCS2018AnnouncementsLocation:PhoenixBallroom

13:30-15:30 Thursday, June 8, 2017 Tutorial2:SensorCloud:ACloudofSensorNetworks(until14:30)Location:Charleston1

SanjayMadria(MissouriUniversityofScienceandTechnology) PlenaryPanelontheConvergenceofBigData,IoT/CPS,andSCC(Smart&ConnectedCommunities)Location:PhoenixBallroom

ThekeywordsofBigData,InternetofThings(IoT),Cyber-PhysicalSystems(CPS),andSmart&ConnectedCommunities(a.k.a.SmartCitiesandSmartPlanet)havebeenaroundforquiteafewyears.Theystartedfromdifferentdisciplines:

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• Bigdataoriginatedfromscientificsensors(e.g.,satellites)andenterpriseapplications(e.g.,Walmartcashregisters).

• IoT/CPSstartedfromwirelesssensornetworksandreal-timesystems(e.g.,theCPSweekseriesofconferences)• SmartPlanet,SmartCities,andSCCstartedfromAutonomicComputing(withthesekeywordspopularized

initiallybyIBM)

Theseresearchfieldsandapplicationareashavebeenexpandingtremendouslyinrecentyears,withsignificantandgrowingoverlapsamongthem.Webelievetheseoverlapsrepresenthugeopportunitiesforinnovativeresearchandpotentialsocietalimpact.Atthesametime,theyalsocreateunprecedentedresearchchallengesthatsurpassthetraditionalconfinesofeachoriginaldiscipline.

Assimpleexamples,bigdataanalyticsandreal-timeIoTsensor-baseddecisionmaking(individually)havebeenthefrontrunnersmartapplicationsshowcasedbySmartCityprojects.However,theircombinationhasseenlimitedintegrationoftechniquesfrombothfields.Anexampleofoverlappingareasisstreaminganalyticsofreal-timeIoTsensors,whichtypicallyusebigdatatoolsatfinetimegranularity(e.g.,ApacheSparkandStorm)withlingeringlimitationsinreal-timeguaranteesandmachinelearningcapabilitiesneededforsmartapplications.Aconcreteexampleofchallengeapplicationsistheautomatedreal-timetrackingofmovingentitiesacrossnetworksofvideocameras.Althoughsuchtrackingisdoneroutinelybyhumans,itsautomationrequiressignificantadvancesintheintegrationofvision/machinelearningtools(objectrecognitionfromvideoimages)withstreaminganalytics(parallelprocessingofvideoimagesfromrelatedcameras).

Thepanelwilldiscusstheoverlappingareasamongbigdata,IoT/CPS,andSCC/SmartCities,withemphasisontheresearchopportunitiesandtechnicalchallenges.

PanelModerator:CaltonPu

CaltonPuCalton'sresearchinterestsareintheareasofservicecomputing,distributedandcloudcomputing,integrationandveracityofbigdata.Hiscurrentprojectsincludecloudcomputing(Elba)andbigdata(GRAIT-DM)research.Usingexperimentaldatafromrealisticbenchmarks,theElbaprojectstudiestheinterestingphenomenasuchasveryshortbottlenecksthathavelargeimpactonn-tiersystemresponsetime.TheGRAIT-DMprojectcollectsrealworlddatafromsocialsensors(e.g.,TwitterandYouTube)andphysicalsensors(e.g.,USGSGSNandNASATRMM)todetectphysicaleventsandmanagereal-timeinformationonthem.ThesponsorsforCaltonPu'sresearchincludebothgovernmentfundingagenciessuchasNSF,andcompaniesfromindustrysuchasHP,Fujitsu,IBM,andIntel.Heisaco-directorofCenterforExperimentalResearchinComputerSystems(CERCS),andaffiliatefacultyofInstituteforInformationSecurityandPrivacy(IISP)atGeorgiaTech.HeisalsothedirectorofRCNonBigDataforSmartCities,withcollaborationsaroundtheworld.Positionsavailable:GeorgiaTechisrecruitinggoodgraduatestudents.

Panelists

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KarlAbererKarlAbererreceivedhisPhDinmathematicsin1991fromtheETHZürich.From1991to1992hewaspostdoctoralfellowattheInternationalComputerScienceInstitute(ICSI)attheUniversityofCalifornia,Berkeley.In1992,hejoinedtheIntegratedPublicationandInformationSystemsinstitute(IPSI)ofGMDinGermany,wherehewasleadingtheresearchdivisionOpenAdaptiveInformationManagementSystems.In2000hejoinedEPFLasfullprofessor.Since2005heisthedirectoroftheSwissNationalResearchCenterforMobileInformationandCommunicationSystems(NCCR-MICS,www.mics.ch).HeismemberoftheeditorialboardsofVLDBJournal,ACMTransactiononAutonomousandAdaptiveSystemsandWorldWideWebJournal.HehasbeenconsultingfortheSwissgovernmentinresearchandsciencepolicyasamemberoftheSwissResearchandTechnologyCouncil(SWTR)from2003-2011.

SrinivasAluruSrinivasAluruisaprofessorintheSchoolofComputationalScienceandEngineeringwithintheCollegeofComputingatGeorgiaInstituteofTechnology.Heco-leadstheGeorgiaTechStrategicInitiativeinDataEngineeringandScience.Heconductsresearchinhighperformancecomputing,bioinformaticsandsystemsbiology,combinatorialscientificcomputing,andappliedalgorithms.Hepioneeredthedevelopmentofparallelmethodsincomputationalbiology,andcontributedtotheassemblyandanalysisofcomplexplantgenomes.Hisgroupiscurrentlyfocusedondevelopingbioinformaticsmethodsforhigh-throughputDNAsequencing,particularlyerrorcorrectionandgenomeassembly.Insystemsbiology,hisgroupisworkingonnetworkinferencemethodsusingmutualinformationandBayesianapproaches,andnetworkanalysistechniquestofurthertheknowledgeofpartiallycharacterizedpathways.HiscontributionsinscientificcomputinglieinparallelFastMultipoleMethod,domaindecompositionmethods,spatialdatastructures,andapplicationsincomputationalelectromagneticsandmaterialsinformatics.AluruisaFellowoftheAmericanAssociationfortheAdvancementofScience(AAAS)andtheInstituteforElectricalandElectronicEngineers(IEEE).HeisarecipientoftheNSFCareeraward(1997),IBMfacultyaward(2002),andSwarnajayantifellowshipfromtheGovernmentofIndia(2007).HeservesontheeditorialboardsoftheIEEETransactionsonParallelandDistributedSystems,theJournalofParallelandDistributedComputing,andtheInternationalJournalofDataMiningandBioinformatics.

KennethCalvertKenCalvertisDivisionDirectorforComputerandNetworkSystemsintheComputerandInformationScienceandEngineering(CISE)DirectorateattheNationalScienceFoundation.HeisonrotationfromtheUniversityofKentucky,whereheisGartnerGroupProfessorinNetworkEngineeringintheDepartmentofComputerScience.Hisresearchdealswiththedesignandimplementationofadvancednetworkprotocolsandservices,withparticularinterestinroutingandincentivesinfuturenetworkarchitectures.HereceivedhisPh.D.incomputersciencefromtheUniversityofTexasatAustin.HeholdsaM.S.incomputersciencefromStanfordUniversityandaB.S.incomputerscienceandengineeringfromtheMassachusettsInstituteofTechnology.PriortohisappointmentattheUniversityofKentucky,hewasaMemberoftheTechnicalStaffatBellLaboratoriesinHolmdel,NJ,andservedonthefacultyintheCollegeofComputing

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attheGeorgiaInstituteofTechnology.HeisanIEEEFellowandamemberoftheACM.

C.MohanDr.C.MohanhasbeenanIBMresearcherfor35yearsinthedatabasearea,impactingnumerousIBMandnon-IBMproducts,theresearchandacademiccommunities,andstandards,especiallywithhisinventionoftheARIESfamilyofdatabaselockingandrecoveryalgorithms,andthePresumedAbortcommitprotocol.ThisIBM(1997),andACM/IEEE(2002)FellowhasalsoservedastheIBMIndiaChiefScientistfor3years(2006-2009).InadditiontoreceivingtheACMSIGMODInnovationAward(1996),theVLDB10YearBestPaperAward(1999)andnumerousIBMawards,MohanwaselectedtotheUSandIndianNationalAcademiesofEngineering(2009),andwasnamedanIBMMasterInventor(1997).ThisDistinguishedAlumnusofIITMadras(1977)receivedhisPhDattheUniversityofTexasatAustin(1981).Heisaninventorof50patents.HeiscurrentlyfocusedonBigData,HTAPandBlockchaintechnologies.In2016,hewasnamedaDistinguishedVisitingProfessorofChina’sprestigiousTsinghuaUniversity.HehasservedontheadvisoryboardofIEEESpectrum,andonnumerousconferenceandjournalboards.MohanisafrequentspeakerinNorthAmerica,EuropeandIndia,andhasgiventalksin40countries.Heisveryactiveonsocialmediaandhasahugenetworkoffollowers.MoreinformationcouldbefoundintheWikipediapageathttp://bit.ly/CMwIkP

ManishParasharManishParasharisDistinguishedProfessorofComputerScienceatRutgers,TheStateUniversityofNewJerseyUniversity.HeisalsothefoundingDirectoroftheRutgersDiscoveryInformaticsInstitute(RDI2)andTheAppliedSoftwareSystemsLaboratory(TASSL),FullMember(ClinicalInvestigationsandPrecisionTherapeuticsProgram)oftheRutgersCancerInstituteofNewJersey,andisAssociateDirectorattheRutgersCenterforInformationAssurance(RUCIA).HealsohasaJointFacultyAppointmentwithOakRidgeNationalLaboratory(ORNL),andisVisitingProfessorintheFacultyofBusiness,Computing&Law,UniversityofDerby,UK.Heco-foundedandwasCo-DirectoroftheCloudandAutonomicComputingCenter(CAC)NSFIUCRCatRutgers(CAC@Rutgers)between2008and2013.AtRutgers,heled(withProf.H.Berman)thestrategicplanningeffortsinResearchComputingandservedastheInterimAssociateVicePresidentofResearchComputingbetween2015–2016tooverseetheestablishmentoftheRutgersOfficeofAdvancedResearchComputing(OARC).HeisalsocurrentlytheLeadPIforCyberinfrastructurefortheNSFOceanObservatoriesInitiative.

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Research Track Paper Abstracts

Research1:DistributedFaultToleranceandDependabilityTimely,Reliable,andCost-EffectiveInternetTransportServiceusingDisseminationGraphs AmyBabay(JohnsHopkinsUniversity),EmilyWagner(JohnsHopkinsUniversity,LTNGlobalCommunications),MichaelDinitz(JohnsHopkinsUniversity),YairAmir(JohnsHopkinsUniversity,LTNGlobalCommunications)

Emergingapplicationssuchasremotemanipulationandremoteroboticsurgeryrequirecommunicationthatisbothtimelyandreliable,buttheInternetnativelysupportsonlycommunicationthatiseithercompletelyreliablewithnotimelinessguarantees(e.g.TCP)ortimelywithbest-effortreliability(e.g.UDP).Wepresentanoverlaytransportservicethatcanprovidehighlyreliablecommunicationwhilemeetingstringenttimelinessguarantees(e.g.130msround-triplatencyacrosstheUS)overtheInternet.Toenableroutingschemesthatcansupportthenecessarytimelinessandreliability,weintroducedisseminationgraphs,providingaunifiedframeworkforspecifyingroutingschemesrangingfromasinglepath,tomultipledisjointpaths,toarbitrarygraphs.Weconductanextensiveanalysisofrealworldnetworkdata,findingthataroutingapproachusingtwodisjointpathsperformswellinmostcases,andthatcaseswheretwodisjointpathsdonotperformwelltypicallyinvolveproblemsaroundasourceordestination.Basedonthisanalysis,wedevelopatimelydissemination-graph-basedroutingmethodthatcanaddtargetedredundancyinproblematicareasofthenetwork.Thisapproachcancoverover99%oftheperformancegapbetweenatraditionalsingle-pathapproachandanoptimal(butprohibitivelyexpensive)scheme,whiletwodynamicdisjointpathscoverabout70%ofthisgap,andtwostaticdisjointpathscoverabout45%.Thisperformanceimprovementisobtainedatacostincreaseofabout2%overtwodisjointpaths.

Pronto:EfficientTestPacketGenerationforDynamicNetworkDataPlanes YuZhao(UniversityofKentucky),HuazheWang(UniversityofCaliforniaatSantaCruz),XinLin(UniversityofCaliforniaatSantaCruz),TingtingYu(UniverstiyofKentucky),ChenQian(UniversityofCaliforniaatSantaCruz)

Computernetworksarebecomingincreasinglycomplextodayandthuspronetovariousnetworkfaults.Traditionaltestingtools(e.g.,ping,traceroute)thatofteninvolvesubstantialmanualefforttouncoverfaultsareinefficient.Thispaperfocusesonfaultdetectionofthenetworkdataplaneusingtestpackets.Existingsolutionsoftestpacketgenerationeithertakeverylongtime(e.g.,morethanonehour)tocompleteorgeneratetoomanytestpacketsthatmayhurtregulartraffic.Inthispaper,wepresentPronto,anautomatedtestpacketgenerationtoolthatgeneratestestpacketstoexercisedataplanerulesintheentirenetworkinashorttime(e.g.,severalseconds)andcanquicklyreacttorulechangesduetonetworkdynamics.Inaddition,Prontominimizesthenumberoftestpacketsbyallowingapackettotestmultiplerulesatdifferentswitches.TheperformanceevaluationusingtworealnetworkdataplanerulesetsshowsthatProntoisfasterthanarecentlydevelopedtoolbymorethantwoordersofmagnitude.Prontocanupdatetheprobesforrulechangesusinglessthan1mswhileexistingmethodshavenosuchupdatefunction.

Agar:ACachingSystemforErasure-CodedData RalucaHalalai(UniversityofNeuchâtel),PascalFelber(UniversityofNeuchâtel),Anne-MarieKermarrec(INRIA),FrançoisTaïani(IRISA)

Erasurecodingisanestablisheddataprotectionmechanism.Itprovideshighresiliencywithlowstorageoverhead,whichmakesitveryattractivetostoragesystemsdevelopers.Unfortunately,whenusedinadistributedsetting,erasurecodinghampersastoragesystemsperformance,becauseitrequiresclientstocontactseveral,possiblyremotesitesinordertoretrievetheirdata.Thishashinderedtheadoptionoferasurecodinginpractice,limitingitsusetocold,archivaldata.Recentresearchshowedthatitisfeasibletouseerasurecodingforhotdataaswell,thusopeningupnewperspectivesforimprovingerasurecodedstoragesystems.Inthispaper,weaddresstheproblemofminimizingaccesslatencyinerasure-codedstorage.WeproposeAgaranovelcachingsystemtailoredforerasure-codedcontent.Agaroptimizesthecontentsofthecachebasedonliveinformationregardingdatapopularityandaccesslatencytodifferentdatastoragesites.Oursystemadaptsadynamicprogrammingalgorithmtooptimizethechoiceofdatablocksthatarecached,usinganapproachakintoKnapsackalgorithms.WecompareAgartotheclassicalLeastRecentlyUsedandLeastFrequentlyUsedcacheevictionpolicies,whilevaryingtheamountofdatacachedbetweenadatachunkandawholereplicaoftheobject.WeshowthatAgarcanachieve16%to41%lowerlatencythansystemsthatuseclassicalcachingpolicies.

Highperformancerecoveryforparallelstatemachinereplication OdoricoMendizabal(FURG),FernandoLuisDotti(PUCRS),FernandoPedone(UniversityofLugano)

Statemachinereplicationisafundamentalapproachtohighavailability.Despitethevastliteratureonthetopic,relativelyfewstudieshaveconsideredtheissuesinvolvedinrecoveringfaultyreplicas.Recoveringareplicarequires(a)retrievingandinstallinganup-to-datereplicacheckpoint,and(b)restoringandre-executingthelogofcommandsnotreflectedinthecheckpoint.Paralleltechniquestostatemachinereplicationrenderrecoveryparticularlychallengingsincethroughputundernormalexecution(i.e.,intheabsenceoffailures)isveryhigh.Consequently,thelogofcommandsthatneedtobeapplieduntilthereplicaisavailableistypicallylarge,whichdelaysrecovery.Inthispaper,wepresenttwotechniquestooptimizerecoveryinparallelstatemachinereplication.Thefirsttechniqueallowsnewcommandstoexecuteconcurrentlywiththeexecutionofloggedcommands,beforereplicasarecompletelyupdated.Thesecondtechniqueintroduceson-demandstaterecovery,whichallowssegmentsofacheckpointtoberecoveredconcurrently.

OnDataParallelismofErasureCodinginDistributedStorageSystems JunLi(UniversityofToronto),BaochunLi(UniversityofToronto)

Deployedinvariousdistributedstoragesystems,erasurecodinghasdemonstrateditsadvantagesoflowstorageoverheadandhighfailuretolerance.Typicallyinanerasurecodeddistributedstoragesystem,systematicmaximumdistanceseperable(MDS)codesarechosensincetheoptimalstorageoverheadcanbeachievedandmeanwhiledatacanbereaddirectlywithoutdecodingoperations.However,dataparallelismofexistingMDScodesislimited,becausewecanonlyreaddatafromsomespecificserversinparallelwithoutdecodingoperations.Inthispaper,weproposeCarouselcodes,designedtoallowdatatobereadfromanarbitrarynumberofserversinparallelwithoutdecoding,whilepreservingtheoptimalstorageoverheadofMDScodes.Furthermore,Carouselcodescanachievetheoptimalnetworktraffictoreconstructanunavailableblock.WehaveimplementedaprototypeofCarouselcodesonApacheHadoop.OurexperimentalresultshavedemonstratedthatCarouselcodescanmakeMapReducejobsfinishwithalmost50%lesstimeandreducedataaccesslatencysignificantly,withacomparablethroughputintheencodinganddecodingoperationsandnoadditionalsacrificeoffailuretoleranceorthenetworkoverheadtoreconstructunavailabledata.

MeteorShower:MinimizingRequestLatencyforMajorityQuorum-basedDataConsistencyAlgorithmsinMultipleDataCenters

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YingLiu(KTHRoyalInstituteofTechnology),XiGuan(KTHRoyalInstituteofTechnology),VladimirVlassov(KTHRoyalInstituteofTechnology),SeifHaridi(KTHRoyalInstituteofTechnology)

Withtheincreasingpopularityofservingandstoringdatainmultipledatacenters,weinvestigatetheefficiencyofmajorityquorum-baseddataconsistencyalgorithmsunderthisscenario.Becauseofthefailure-pronenatureofdistributedstoragesystems,majorityquorum-baseddataconsistencyalgorithmsbecomeoneofthemostwidelyadoptedapproaches.Inthispaper,weproposetheMeteorShowerframework,whichprovidesfaulttolerantread/writekey-valuestorageserviceacrossmultipledatacenterswithsequentialconsistencyguarantees.Amajorfeatureisthatmostreadoperationsareexecutedlocallywithinasingledatacenter.Thisresultsinloweringreadlatencyfromhundredsofmillisecondstotensofmilliseconds.ThedataconsistencyalgorithminMeteorShoweraugmentsmajorityquorum-basedalgorithms.Thus,itkeepsallthedesirablepropertiesofmajorityquorums,suchasfaulttolerance,balancedload,etc.AnimplementationofMeteorShowerontopofCassandraisdeployedandevaluatedinmultipledatacentersusingtheGoogleCloudPlatform.EvaluationsofMeteorShowerframeworkhaveshownthatitcanconsistentlyservereadrequestswithoutpayingthecommunicationdelaysamongreplicasmaintainedinmultipledatacenters.Asaresult,weareabletoimprovethelatencyofreadrequestsfromhundredsofmillisecondstotensofmillisecondswhileachievingthesamelatencyonwriterequestsandthesamefaulttoleranceguarantee.Thus,MeteorShowerisoptimizedforreadintensiveworkloads.

Research2:DistributedOperatingSystemsandMiddlewareLSbM-tree:Re-enablinghigh-speedcachinginDataManagementforMixedReadsandWrites DejunTeng(TheOhioStateUniversity),LeiGuo(Google),RubaoLee(TheOhioStateUniversity),FengChen(LouisianaStateUniversity),SiyuanMa(TheOhioStateUniversity),XiaodongZhang(TheOhioStateUniversity),YanfengZhang(NortheasternUniversity)

LSM-treehasbeenwidelyusedindatamanagementproductionsystemsforwrite-intensiveworkloads.However,asreadandwriteworkloadsco-existunderLSM-tree,dataaccessescanexperiencelonglatencyandlowthroughputduetotheinterferencestobuffercachingfromthecompaction,amajorandfrequentoperationinLSM-tree.Afteracompaction,theexistingdatablocksarereorganizedandwrittentootherlocationsondisks.Asaresult,therelateddatablocksthathavebeenloadedinthebuffercacheareinvalidatedsincetheirreferencingaddressesarechanged,causingseriousperformancedegradations.Inordertore-enablehigh-speedbuffercachingduringintensivewrites,weproposeLog-Structuredbuffered-Mergetree(simplifiedasLSbM-tree)byaddingacompactionbufferondisks,tominimizethecacheinvalidationsonbuffercachecausedbycompactions.Thecompactionbufferefficientlyandadaptivelymaintainsthefrequentlyvisiteddatasets.InLSbM,stronglocalityobjectscanbeeffectivelykeptinthebuffercachewithminimumorwithoutharmfulinvalidations.Withthehelpofasmallon-diskcompactionbuffer,LSbMachievesahighqueryperformancebyenablingeffectivebuffercaching,whileretainingallthemeritsofLSM-treeforwrite-intensivedataprocessing,andprovidinghighbandwidthofdisksforrangequeries.WehaveimplementedLSbMbasedonLevelDB.Weshowthatwithastandardbuffercacheandaharddisk,LSbMcanachieve2xperformanceimprovementoverLevelDB.WehavealsocomparedLSbMwithotherexistingsolutionstoshowitsstrongeffectiveness.

IncrementalTopologyTransformationforPublish/SubscribeSystemsUsingIntegerProgramming PooyaSalehi(TechnicalUniversityofMunich),KaiwenZhang(TechnicalUniversityofMunich),Hans-ArnoJacobsen(UniversityofToronto)

Distributedoverlay-basedpublish/subscribesystemsprovideaselective,scalable,anddecentralizedapproachtodatadissemination.Duetothedynamiccommunicationflowsbetweendataproducersandconsumers,theoverlaytopologyofsuchsystemscanbecomeinefficientovertimeandthereforerequiresadaptationtotheexistingload.Existingstudiesproposealgorithmstodesignoverlaytopologieswhichareoptimizedforspecificworkloads.However,theproblemofgeneratingaplantoincrementallytransformthecurrenttopologytoanoptimizedonehasbeenlargelyignored.Inthispaper,wepresentIPITT,anapproachbasedonintegerprogrammingfortheincrementaltopologytransformation(ITT)problem.Giventhecurrenttopologyandatargettopology,IPITTgeneratesatransformationplanwithaminimalnumberofstepsinordertolessenservicedisruption.Furthermore,weintroduceaplanexecutionmechanismandevaluateourapproachonanexistingpublish/subscribesystem.Basedonourevaluation,IPITTcanreduceplancomputationtimebyafactorof10andgeneratesplanswithanexecutiontimeupto55%shorterthanthoseofexistingapproaches.

milliScope:aFine-GrainedMonitoringFrameworkforPerformanceDebuggingofn-TierWebServices Chien-AnLai(GeorgiaInstituteofTechnology),JoshKimball(GeorgiaInstituteofTechnology),TaoZhu(GeorgiaInstituteofTechnology),QingyangWang(LouisianaStateUniversity),CaltonPu(GeorgiaInstituteofTechnology)

Moderndistributedsystemsareoftenconsideredtobeblack-boxesthatgreatlylimitthepotentialtounderstandbehaviorsatthelevelofdetailnecessarytodiagnosesomeofthemostimportanttypesofperformanceproblems.Recentlyresearchershavefoundabnormalresponsetimedelay,onetotwoorderofmagnitudelongertimethantheaverageresponsetime,existsinshortperiodandcauseseconomicallossforserviceproviders.Theseveryshortbottlenecksarehardtodetectduetoitsshortlivespananditsvarietyofpossiblereasons.Inthispaper,weproposemilliScope(mScope),thefirstmillisecond-granularitysoftware-basedresourceandeventmonitoringfordistributedsystemsthatachievesbothperformance,lowoverheadathighfrequency,andhighaccuracymatchedwithotherfirmwaremonitoringtool.Morespecifically,milliScopeisafine-grainedmonitoringframeworktocollaboratemultiplemScopeMonitorsforeventandresourcemonitoringtoreconstructtheflowofeachclientrequestandprofileexecutionperformanceinadistributedsystem.WeutilizetheresourcemScopeMonitorsforsystemresourcemonitoring,andwedevelopourowneventmScopeMonitorstoidentifytheexecutionboundaryinalightweight,preciseandsystematicmethodology.Thesemanticandsyntacticofthesemonitoringlogswitharbitraryformatsareenrichedbyourmulti-stagedatatransformationtool,mScopeDataTransformer,whichunifiesthediversemonitoringlogsintoadynamicdatawarehouse,mScopeDB,foradvancedanalysis.WeconductseveralillustrativescenariosinwhichmilliScopesuccessfullydiagnosestheresponsetimeanomaliescausedbyveryshortbottlenecksusingarepresentativewebapplicationbenchmark(RUBBoS).Besides,wevalidatetheaccuracyofoureventmScopeMonitorsanddemonstrateavailabilityandflexibilityofmilliScopethroughseveralevaluations.

Stark:OptimizingIn-MemoryComputingForDynamicDatasetCollections ShenLi(IBMResearch),MdTanvirAlAmin(UIUC),RaghuGanti(IBMResearch),MudhakarSrivatsa(IBMResearch),ShaohanHu(IBMResearch),YiranZhao(UIUC),TarekAbdelzaher(UIUC)

Emergingdistributedin-memorycomputingframeworks,suchasApacheSpark,canprocessahugeamountofcacheddatawithinseconds.Thisremarkablyhighefficiencyrequiresthesystemtowellbalancedataacrosstasksandensuredatalocality.However,itischallengingtosatisfytheserequirementsforapplicationsthatoperateonacollectionofdynamicallyloadedandevicteddatasets.Thedynamicsmayleadtotime-varyingdatavolumeanddistribution,whichwouldfrequentlyinvokeexpensivedatare-partitionandtransferoperations,resultinginhighoverheadandlargedelay.Toaddressthisproblem,wepresentStark,a

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systemspecificallydesignedforoptimizingin-memorycomputingondynamicdatasetcollections.Starkenforcesdatalocalityfortransformationsspanningmultipledatasets(e.g.,joinandcogroup)toavoidunnecessarydatareplicationsandshuffles.Moreover,toaccommodatefluctuatingdatavolumeandskeweddatadistribution,Starkdeliverselasticityintopartitionstobalancetaskexecutiontimeandreducejobmakespan.Finally,Starkachievesboundedfailurerecoverylatencybyoptimizingthedatacheckpointingstrategy.Evaluationsona50-serverclustershowthatStarkreducesthejobmakespanby4Xandimprovessystemthroughputby6XcomparedtoSpark.

CRESON:CallableandReplicatedSharedObjectsoverNoSQL PierreSutra(TélécomSudParis,CNRS,UniversitéParis-Saclay,France),EtienneRivière(UniversityofNeuchatel),CristianCotes(UniversitatRoviraiVirgili),MarcSánchezArtigas(UniversitatRoviraiVirgili),PedroGarciaLopez(UniversitatRoviraiVirgili),EmmanuelBernard(RedHat),WilliamBurns(RedHat),GalderZamarreno(RedHat)

TheabilitytoshareandpersistobjectssimplifiesthedesignofapplicationsinCloudenvironments.StoringobjectsonaNoSQLdatabaseensuresavailabilityandscalability.WhenObject-NoSQLMappingisperformedattheclientside,objectsthatareaccessedconcurrentlyarerepeatedlyconvertedbetweentheirin-memoryandserializedrepresentations.Thisnegativelyimpactsperformanceandincreasesreplicationcosts.WedescribeinthispaperthedesignofCRESON,asystemsupportingcallableobjectsoverNoSQL,inwhichapplicationobjectsaremappedandinstantiateddirectlyonthestoragenodes.CRESONsupportscompositionbyreferenceandensuresstrongconsistency.ObjectsarereplicatedandmaintainedcoherentusingStateMachineReplication.TheimplementationofCRESONleveragesthesupportofalistenablekey-valuestore(LKVS),anovelNoSQLstorageabstractionthatweintroduceinthispaper.WediscusstheperformanceandcomplexityofusingCRESONwiththeexampleoftheportageofapersonalcloudstorageservice,initiallydevelopedusingobject-relationalmappingoverashardedPostgreSQLdatabase.OurresultsshowthatCRESONoffersasimplerprogrammingexperiencebothintermsoflearningtimeandlinesofcode,whileperformingbetteronaverageandbeingmorescalable.

VirtualizedNetworkCodingFunctionsontheInternet LinquanZhang(UniversityofCalgary),ShangqiLai(TheUniversityofHongKong),ChuanWu(TheUniversityofHongKong),ZongpengLi(UniversityofCalgary),ChuanxiongGuo(MicrosoftResearch)

Networkcodingisafundamentaltoolthatenableshighernetworkcapacityandlowercomplexityinroutingalgorithms,byencouragingthemixingofinformationflowsinthemiddleofanetwork.ImplementingnetworkcodinginthecoreInternetissubjecttopracticalconcerns,sinceInternetroutersareoftenoverwhelmedbypacketforwardingtasks,leavinglittleprocessingcapacityforcodingoperations.Inspiredbytherecentparadigmofnetworkfunctionvirtualization,weproposeimplementingnetworkcodingasanewnetworkfunction,anddeployingsuchcodingfunctionsingeo-distributedclouddatacenters,topracticallyenablenetworkcodingontheInternet.Wetargetmulticastsessions(includingunicastflowsasspecialcases),strategicallydeployrelaynodes(networkcodingfunctions)inselecteddatacentersbetweensendersandreceivers,andembracehighbandwidthefficiencybroughtbynetworkcodingwithdynamiccodingfunctiondeployment.Wedesignandimplementthenetworkcodingfunctionontypicalvirtualmachines,featuringefficientpacketprocessing.Weproposeanefficientalgorithmforcodingfunctiondeployment,scalinginandout,inthepresenceofsystemdynamics.Real-worldimplementationonAmazonEC2andLinodedemonstratessignificantthroughputimprovementandhigherrobustnessofmulticastviacodingfunctionsaswellasefficiencyofthedynamicdeploymentandscalingalgorithm.

Research3:SecurityandPrivacyinDistributedSystemsIConsensusRobustnessandTransactionDe-AnonymizationintheRippleCurrencyExchangeSystem AdrianoDiLuzio(SapienzaUniversityofRome),AlessandroMei(SapienzaUniversityofRome),JulindaStefa(SapienzaUniversityofRome)

Distributedfinancialsystemsareradicallychangingthewaywedobusinessandspendourmoney.Ripple,inparticular,isuniqueinitskind.Itisbuiltonconsensusandtrustamongitspeersand,differentlyfrommanyothersystems,itallowsexchangingbothfiatcurrenciesandgoods.Itdoessobystoringtheaccountsofitsusers,theirbalances,andallthetransactionsinadistributedledger,publiclyaccessible.Inthispaperweperformforthefirsttimeanin-depthstudyoftheRippleexchangesystemanditspublicdistributedledger.Weanalyzepayments,thestructureofpaymentpaths,andtheroleofimportantpeersinthesystemsuchasGateways(theequivalentofBanks)andMarketMakersthatallowcross-currencyexchangeamongusers.TocompletethestudyoftheecosystemweanalyzetheinternalstreamofeventsinRippleandshowthatthewholesystemreliesonasurprisinglysmallnumberofactivevalidators,raisingseveralconcernsontherobustnessandontheactualfairnessofthesystem.Finally,weshowhowdistributedfinancialsystemscanjeopardizetheprivacyoftheirusers.Byexaminingthefirst3yearsofRipplehistory(morethan500GBofdata),weshowthatevenapproximateinformationonasinglepaymentcanuncover,withincredibleaccuracy,thewholefinanciallifeoftheuser.Forexample,thisallowsanyonewhooverhearsourorderforaLatteatourfavouritebartogaincompleteandunlimitedaccesstoourbalance,ourpreviousandfuturepayments,ourmonthlyincome,aswellastheplaceswhereweshopandthepeoplewetrust.

LearningprivacyhabitsofPDSowners BikashSingh(UniversityofInsubria),BarbaraCarminati(universityofinsubria),ElenaFerrari(universityofinsubria)

TheconceptofPersonalDataStorage(PDS)hasrecentlyemergedasanalternativeandinnovativewayofmanagingpersonaldataw.r.t.theservice-centriconecommonlyusedtoday.ThePDSoffersauniquelogicalrepository,allowingindividualstocollect,store,andgiveaccesstotheirdatatothirdparties.TheresearchonPDShassofarmainlyfocusedontheenforcementmechanisms,thatis,onhowuserprivacypreferencescanbeenforced.Incontrast,thefundamentalissueofpreferencespecificationhasbeensofarnotdeeplyinvestigated.Inthispaper,wedoastepinthisdirectionbyproposingdifferentlearningalgorithmsthatallowafine-grainedlearningoftheprivacyaptitudesofPDSowners.Thelearnedmodelsarethenusedtoanswerthirdpartyaccessrequests.Theextensiveuserstudieswehaveperformedshowtheeffectivenessoftheproposedapproach.

City-Hunter:HuntingSmartphonesinUrbanAreas XuefengLiu(HongKongPolytechnicUniversity),JiaqiWen(HongKongPolytechnicUniversity),ShaojieTang(UniversityofTexasatDallas),JiannongCao(HongKongPolytechnicUniversity),JiaxingShen(HongKongPolytechnicUniversity)

ThesecurityissueofpublicWiFiisgainingmoreandmoreconcern.Bylisteningtoproberequests,anadversarycanobtaintheSSIDlistoftheAPstowhichasmartphonepreviouslyconnected,andutilizesthisinformationtotrickthesmartphoneintoassociatingtoit.However,withtheenhancementofsecuritylevel,mostsmartphonesnowdonotproactivelydisclosetheirSSIDlists,makingtheseattacksobsolete.Inthispaper,weproposeCity-Hunter,anattackerthatcanlurenearbysmartphoneswithoutknowingtheirSSIDinformation.City-HunterestablishesandmaintainsanSSIDdatabasebyintegratingbothofflineandonlineinformation.Meanwhile,itsmartlychoosessomeSSIDstohitasmartphoneaccordingtothepastrecordandfreshness.WeevaluatetheperformanceofCity-

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Hunterindifferentpublicplaces.TheresultsdemonstratethatCity-Hunterisabletosuccessfullyhit12%_18%smartphoneswithoutknowingtheirSSIDinformation,whichisabout4_8timesimprovementcomparedtothesimilarattackslikeKARMAandMANA.

WhenSeeingIsn'tBelieving:OnFeasibilityandDetectabilityofScapegoatinginNetworkTomography ShangqingZhao(UniversityofSouthFlorida),ZhuoLu(UniversityofSouthFlorida),CliffWang(NorthCarolinaStateUniversity/ArmyResearchOffice)

Networktomographyisavitaltooltoestimatelinkqualitiesfromend-to-endnetworkmeasurements.Animplicitassumptioninnetworktomographyisthatobservedmeasurementsindeedreflecttheaggregateoflinkperformance(i.e.,seeingisbelieving).However,itisnotguaranteedtodaythatthereexistsnoanomaly(e.g.,maliciousautonomoussystemsandinsiderthreats)inlarge-scalenetworks.Maliciousnodescanintentionallymanipulatelinkmetricsviadelayingordroppingpacketstoaffectmeasurements.Willsuchanassumptionrenderavulnerabilitywhenfacingattackers?Theproblemisofessentialimportanceinthatnetworktomographyisdevelopedtowardseffectivenetworkdiagnosticsandfailurerecovery.Inthispaper,wedemonstratethatthevulnerabilityisrealandproposeanewattackstrategy,calledscapegoating,inwhichmaliciousnodescansubstantiallydamageanetwork(e.g.,delayingpackets)andatthesametimemaliciouslymanipulateend-to-endmeasurementresultssuchthatalegitimatenodeismisleadinglyidentifiedastherootcauseofthedamage(therebybecomingascapegoat)undernetworktomography.Weformulatethreebasicscapegoatingapproachesandshowunderwhatconditionsattackscanbesuccessful.Wealsorevealconditionstodetectsuchattacks.Ourtheoreticalandexperimentalresultsshowthatsimplytrustingmeasurementsleadstoscapegoatingvulnerabilities.Thus,existingmethodsshouldberevisitedaccordinglyforsecurityinvariousapplications.

YouCanHearButYouCannotSteal:DefendingagainstVoiceImpersonationAttacksonSmartphones SiChen(UniversityatBuffalo/WestChesterUniversity),KuiRen(UniversityatBuffalo),SixuPiao(UniversityatBuffalo),CongWang(CityUniversityofHongKong),QianWang(WuhanUniversity),JianWeng(JinanUniversity),LuSu(UniveristyatBuffalo),AzizMohaisen(UniversityatBuffalo)

Voice,asaconvenientandefficientwayofinformationdelivery,hasasignificantadvantageovertheconventionalkeyboard-basedinputmethods,especiallyonsmallmobiledevicessuchassmartphonesandsmartwatches.However,thehumanvoicecouldoftenbeexposedtothepublic,whichallowsanattackertoquicklycollectsoundsamplesoftargetedvictimsandfurtherlaunchvoiceimpersonationattackstospoofthosevoicebasedapplications.Inthispaper,weproposethedesignandimplementationofarobustsoftware-onlyvoiceimpersonationdefensesystem,whichistailoredformobileplatformsandcanbeeasilyintegratedwithexistingoff-the-shelfsmartdevices.Inoursystem,weexploremagneticfieldemittedfromloudspeakersastheessentialcharacteristicfordetectingmachine-basedvoiceimpersonationattacks.Furthermore,weuseastate-of-the-artautomaticspeakerverificationsystemtodefendagainsthumanimitationattacks.Ouradvancedsensorydataprocessingtechniqueachievesfastauthenticationspeed(6.1s)whichmakesitsuitableformobileplatforms.Finally,ourevaluationresultsshowthatoursystemachievessignificantlyhighaccuracy(100%)andlow(0%)equalerrorrates(EERs)indetectingthemachinebasedvoiceimpersonationattackonsmartphones.

FlowReconnaissanceviaTimingAttacksonSDNSwitches ShengLiu(UniversityofNorthCarolinaatChapelHill),MichaelReiter(UniversityofNorthCarolinaatChapelHill),VyasSekar(CarnegieMellonUniversity)

Whenencounteringapacketflowforwhichithasnocoveringrule,asoftware-definednetworking(SDN)switchrequestsanappropriaterulefromitscontroller;thisrequestdelaystheroutingoftheflowuntilthecontrollerresponds.Weshowthatthisdelaygivesrisetoatimingsidechannelinwhichanattackercantestfortherecentoccurrenceofatargetflowbyjudiciouslyprobingtheswitchwithforgedflowsandusingthedelaystheysuffertodiscernwhethercoveringruleswerepreviouslyinstalledintheswitch.WedevelopaMarkovmodelofanSDNswitchtopermittheattackertoselectthebestprobe(orprobes)toinferwhetheratargetflowhasrecentlyoccurred.Ourmodelcapturescomplexitiesrelatedtoruleevictionstomakeroomforotherrules;ruletimeoutsduetoinactivity;thepresenceofmultiplerulesthatapplytooverlappingsetsofflows;andrulepriorities.Weshowthatourmodelpermitsdetectionoftargetflowswithconsiderableaccuracyinmanycases.

Research4:CloudComputingandDataCenterSystemsAStudyofLong-TailLatencyinn-TierSystems:RPCvs.AsynchronousInvocations QingyangWang(LouisianaStateUniversity),Chien-AnLai(GeorgiaTech),YasuhikoKanemasa(FujitsuLaboratoriesLtd.),ShungengZhang(LouisianaStateUniversity),CaltonPu(GeorgiaTech)

Long-taillatencyofweb-facingapplicationscontinuestobeaseriousproblem.Mostofthepreviouslypublishedresearchaddressestwoclassesoflonglatencyproblems:unevenworkloadssuchaswebsearch,andresourcesaturationinsinglenodes.Wedescribeanexperimentalstudyofathirdclassoflongtaillatencyproblemsthatarespecifictodistributedsystems:Cross-TierQueueOverflow(CTQO)duetoacombinationofmillibottlenecks(withsub-secondduration)andtightly-coupledserversinn-tiersystems(e.g.,Apache,Tomcat,andMySQL)usingRPC-stylerequest-responsecommunications.Ourexperimentsshowthattheappearanceofmillibottlenecks(e.g.,createdbyshortworkloadbursts)inoneserveroftencausesanotherserver(whichhasnosaturatedresources)inthesynchronousinvocationchaintofillupitsqueues(CTQO)anddroppackets,creatingverylongresponsetimequeries.CTQOcanbereducedoravoidedbyreplacingtheserverdroppingpacketswithanasynchronousserver.Insynchronousn-tiersystemexperiments,longtaillatencyduetoCTQOcanbereproducedconsistentlyatutilizationaslowas43%.Incontrast,whenalln-tierserversarereplacedbyasynchronousversions,CTQOandconsequentdroppedpacketsremainabsentatutilizationlevelsashighas83%,despitethesamemillibottlenecks.

RainorShine?-MakingSenseofCloudyReliabilityData IyswaryaNarayanan(ThePennsylvaniaStateUniversity),BikashSharma(Microsoft),DiWang(Microsoft),SriramGovindan(Microsoft),LauraCaulfield(Microsoft),AnandSivasubramaniam(ThePennsylvaniaStateUniversity),AmanKansal(Microsoft),JieLiu(Microsoft),BadriddineKhessib(Microsoft),KushagraVaid(Microsoft)

Clouddatacentersmustensurehighavailabilityforthehostedapplicationsandfailurescanbethebaneofdatacenteroperators.Understandingthewhat,whenandwhyoffailurescanhelptremendouslytomitigatetheiroccurrenceandimpact.Failurescan,however,dependonnumerousspatialandtemporalfactorsspanninghardware,workloads,supportfacilities,andeventheenvironment.Onehastorelyonfailuredatafromthefieldtoquantifytheinfluenceofthesefactorsonfailures.Towardsthisgoal,wecollectfailuresdataalongwithmanyparametersthatmightinfluencefailuresfromtwolargeproductiondatacenterswithvery

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diversecharacteristics.Weshowthatmultiplefactorssimultaneouslyaffectfailures,andthesefactorsmayinteractinnon-trivialways.Thismakesconventionalapproachesthatstudyaggregatecharacteristicsorsingleparameterinfluences,ratherinaccurate.Instead,webuildamulti-factoranalysisframeworktosystematicallyidentifyinfluencingfactors,quantifytheirrelativeimpact,andhelpinmoreaccuratedecisionmakingforfailuremitigation.Wedemonstratethisapproachforthreeimportantdecisions:sparecapacityprovisioning,comparingthereliabilityofhardwareforvendorselection,andquantifyingflexibilityindatacenterclimatecontrolforcost-reliabilitytrade-offs.

Right-sizingGeo-distributedDataCentersforAvailabilityandLatency IyswaryaNarayanan(ThePennsylvaniaStateUniversity),AmanKansal(Microsoft),AnandSivasubramaniam(ThePennsylvaniaStateUniversity)

Weshowclouddevelopershowtorightsizedatacenter(DC)capacityforgeo-distributedapplicationsdeployedonseveralmulti-megawattDCs,possiblyalsousingmanysmalleredgeDCs.Notethatcapacityconsiderationsforageo-distributedinfrastructuredonotdecomposeintoindividualDCcapacityplanning.WhenedgeDCsareused,heterogeneousavailabilityandcostsaffectthecapacitysplitbetweentheedgeandcoreDCs.Non-uniformspatialdistributionofclientsandinterdependencebetweenlatencyandavailabilityconstraintsmakeitnon-trivialtoprovisiontherightcapacityateachDC.Wedevelopageo-distributedcapacityplanningframeworktocapturethekeyfactorsthatinfluencecapacity,rangingfromapplicationdemandpatterns,latencyandavailabilityrequirements,DCcost-availabilitytrade-offs,anddatareplicationoverheads.WeapplyourframeworktoarealisticapplicationandDCinfrastructuresettingtogatherinsightsintohowcapacityshouldbeprovisionedandallocatedacrossDCsforarepresentativesetofrequirementsandcosts.

PerformanceDrivenResourceSharingMarketsfortheSmallCloud Sung-HanLin(UniversityofSouthernCalifornia),RanjanPal(UniversityofSouthernCalifornia),MarcoPaolieri(UniversityofSouthernCalifornia),LeanaGolubchik(UniversityofSouthernCalifornia)

Small-scaleclouds(SCs)oftensufferfromresourceunder-provisioningduringpeakdemand,leadingtoinabilitytosatisfyservicelevelagreements(SLAs)andconsequentlossofcustomers.OneapproachtoaddressthisproblemisforasetofautonomousSCstoshareresourcesamongthemselvesinacostinducedcooperativefashion,therebyincreasingtheirindividualcapacities(whenneeded)withouthavingtosignificantlyinvestinmoreresources.Acentralproblem(inthiscontext)ishowtoproperlyshareresources(foraprice)toachieveprofitableservicewhilemaintainingcustomerSLAs.Toaddressthisproblem,inthispaper,weproposetheSC-Shareframeworkthatutilizestwointeractingmodels:(i)astochasticperformancemodelthatestimatestheachievedperformancecharacteristicsundergivenSLArequirements,and(ii)amarket-basedgame-theoreticmodelthat(asshownempirically)convergestoefficientresourcesharingdecisionsatmarketequilibrium.Ourresultsincludeextensiveevaluationsthatillustratetheutilityoftheproposedframework.

Fault-scalableVirtualizedInfrastructureManagement MukilKesavan(VMwareInc.),AdaGavrilovska(GeorgiaInstituteofTechnology),KarstenSchwan(GeorgiaInstituteofTechnology)

Large-scalevirtualizeddatacentersrequireconsiderableautomationininfrastructuremanagementinordertooperateefficiently.Automationisimpaired,however,bythefactthatdeploymentsarepronetomultipletypesofsubtlefaultsduetohardwarefailures,softwarebugs,misconfiguration,crashes,performancedegradedhardware,etc.ExistingInfrastructure-as-a-Service(IaaS)managementstacksincorporatelittletonoresiliencemeasurestoshieldendusersfromsuchcloudproviderlevelfailuresandpoorperformance.ThispaperproposesandevaluatesextensionstoIaaSstacksthatmaskfaultsinafault-agnosticmannerwhileensuringthattheoverheadscanbeproportionaltoobservedfailurerates.Wealsodemonstratethatinfrastructureautomationservicesandend-userapplicationscanuseservice-specificknowledge,togetherwithournewinterface,toachievebetteroutcomes.

DeltaCFS:BoostingDeltaSyncforCloudStorageServicesbyLearningfromNFS QuanluZhang(PekingUniversity),ZhenhuaLi(TsinghuaUniversity),ZhiYang(PekingUniversity),ShenglongLi(PekingUniversity),YangzeGuo(PekingUniversity),YafeiDai(PekingUniversity),ShouyangLi(PekingUniversity)

Cloudstorageservices,suchasDropbox,iCloudDrive,GoogleDrive,andMicrosoftOneDrive,havegreatlyfacilitateduserssynchronizingfilesacrossheterogeneousdevices.Amongthem,Dropbox-likeservicesareparticularlybeneficialowingtothedeltasyncfunctionalitythatstrivestowardsgreaternetwork-levelefficiency.However,whendeltasynctradescomputationoverheadfornetwork-trafficsaving,thetradeoffcouldbehighlyunfavorableundersometypicalworkloads.Werefertothisproblemastheabuseofdeltasync.Toaddressthisproblem,weproposeDeltaCFS,anovelfilesyncframeworkforcloudstorageservicesbylearningfromthedesignofconventionalNFS(NetworkFileSystem).Specifically,wecombinedeltasyncwithNFS-likefileRPCinanadaptivemanner,thussignificantlycuttingcomputationoverheadonboththeclientandserversideswhilepreservingthenetwork-levelefficiency.DeltaCFSalsoenablesaneatdesignforguaranteeingcausalconsistencyandfine-grainedversioncontroloffiles.InourFUSE-basedprototypesystem(whichisopen-source),DeltaCFSoutperformsDropboxbygeneratingupto11_lessdatatransferandupto100_lesscomputationoverheadunderconcernedworkloads.

Research5:EdgeandFogComputingCachier:Edge-cachingforrecognitionapplications UtsavDrolia(CarnegieMellonUniversity),KatherineGuo(BellLabs),JiaqiTan(Nokia),RajeevGandhi(CarnegieMellonUniversity),PriyaNarasimhan(CarnegieMellonUniversity)

Recognitionandperception-basedmobileapplications,suchasimagerecognition,areontherise.Theseapplicationsrecognizetheuserssurroundingsandaugmentitwithinformationand/ormedia.Theseapplicationsarelatencysensitive.Theyhaveasoft-realtimenature-lateresultsarepotentiallymeaningless.Ontheonehand,giventhecomputeintensivenatureofthetasksperformedbysuchapplications,executionistypicallyoffloadedtothecloud.Ontheotherhand,offloadingsuchapplicationstothecloudincursnetworklatency,whichcanincreasetheuser-perceivedlatency.Consequently,edge-computinghasbeenproposedtoletdevicesoffloadintensivetaskstoedge-serversinsteadofthecloud,toreducelatency.Inthispaper,weproposeadifferentmodelforusingedge-servers.Weproposetousetheedgeasaspecializedcacheforrecognitionapplicationsandformulatetheexpectedlatencyforsuchacache.Weshowthatusinganedge-serverlikeatypicalweb-cache,forrecognitionapplications,canleadtohigherlatencies.WeproposeCachier,asystemthatusesthecachingmodelalongwithnoveloptimizationstominimizelatencybyadaptivelybalancingloadbetweentheedgeandthecloudbyleveragingspatiotemporallocalityofrequests,usingofflineanalysisofapplications,andonlineestimatesofnetworkconditions.WeevaluateCachierforimage-recognitionapplicationsandshowthatourtechniquesyield3xspeed-upinresponsiveness,andperformaccuratelyoverarangeofoperatingconditions.Tothebestofourknowledge,thisisthefirstworkthatmodelsedge-serversascachesforcomputeintensiverecognitionapplications,andCachieristhefirstsystemthatusesthismodeltominimizelatencyfortheseapplications.

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ContentCentricPeerDataSharinginPervasiveEdgeComputingEnvironments XintongSong(PekingUniversity),YaodongHuang(StonyBrookUniversity),QianZhou(StonyBrookUniversity),FanYe(StonyBrookUniversity),YuanyuanYang(StonyBrookUniversity),XiaomingLi(PekingUniversity)

Theproliferationanddailycongregationofmodernmobiledeviceshavecreatedabundantopportunitiesforpeeredgedevicestosharevaluabledatawitheachother.Theshortcontactdurations,relativelysmallsharingsizes,anduncertaindataavailability,demandagile,lightweightpeerbaseddatasharing.Inthispaper,weproposePeerDataSharing(PDS)thatenablesedgedevicestodiscoverwhichdataexistinnearbypeers,andretrieveinteresteddatarobustlyandefficiently.PDSusesnovellingeringqueries,mixedcastanden-routemessagerewritingtechniquestominimizeredundanttransmissionsandmaximizeopportunisticoverhearingthuscachingindatadiscoveryandretrieval.ExtensiveevaluationsbasedonanAndroidprototypeshowthatPDSdiscoversandretrievesalmost100%dataintensofseconds,andremainsrobustdespitewirelesscontention,simultaneousconsumerrequestsandusermobility.

FLARE:CoordinatedRateAdaptationforHTTPAdaptiveStreaminginCellularNetworks YoungbinIm(UniversityofColoradoatBoulder),JinyoungHan(HanyangUniversity),JiHoonLee(JuniKorea),YoonKwon(Kakao),CarleeJoe-Wong(CarnegieMellonUniversity),TaekyoungKwon(SeoulNationalUniversity),SangtaeHa(UniversityofColoradoatBoulder)

Fogcomputingisanemergingarchitecturethataimstorunapplicationsonmultipledevicesthatlieonacontinuumfromcentralizedcloudserverstopersonaluserdevices.Thesearchitecturesallowapplicationstooptimizeovertheinformationstoredateachtypeofdeviceanddividetheirfunctionalitiesbasedonthedevicecapabilities.Wedemonstratethebenefitsofthisapproachformobilevideostreaming.ExistingHAS(HTTPadaptivestreaming)techniques,usedbypopularvideoserviceproviders,oftensufferfromproblemslikeunstablevideoqualityandsuboptimalresourceutilization.Wefindthatalackofcoordinationpreventsbothclient-andnetwork-sideHAStechniquesfromsolvingthem.However,ourfogapproachcanexploitexistingtelecommunicationAPIs,whichexposenetworkcapabilitiestoapplications,inordertocoordinatebetweentheclientandnetwork.OurcoordinatedHASsolution,FLARE,optimizesthetotalutilityofallclientsinacellwhilemaintainingstablevideoqualityandsupportinguser-anddevice-specificneeds.WeimplementFLAREonacommodityLTEfemtocellandusetheimplementationtoconductthefirstcomparisonofHASplayersonanLTEfemtocell.Byconductingextensiveexperimentsusingthens-3simulator,wealsodemonstratethatFLARE(i)enhancestheaveragevideobitrate,(ii)achievesstablevideoquality,and(iii)balancesthethroughputofsimultaneousvideoanddataflows,comparedtootherrepresentativeHASsolutions.

NetworkedDroneCamerasforSportsStreaming XiaoliWang(PrincetonUniversity),AakankshaChowdhery(PrincetonUniversity),MungChiang(PrincetonUniversity)

Anetworkofdronecamerascanbedeployedtocoverliveevents,suchashigh-actionsportsgameplayedonalargefield,butmanagingnetworkeddronecamerasinreal-timeischallenging.Distributedapproachesyieldsuboptimalsolutionsfromlackofcoordinationbutcoordinationwithacentralizedcontrollerincursround-triplatenciesofseveralhundredsofmillisecondsoverawirelesschannel.Weproposeafog-networkingbasedsystemarchitecturetoautomaticallycoordinateanetworkofdronesequippedwithcamerastocaptureandbroadcastthedynamicallychangingscenesofinterestinasportsgame.Wedesignbothoptimalandpracticalalgorithmstobalancethetradeoffbetweentwometrics:coverageofthemostimportantscenesandstreamedvideobitrate.Tocompensatefornetworkround-triplatencies,thecentralizedcontrollerusesapredictiveapproachtopredictwhichlocationsthedronesshouldcovernext.Thecontrollermaximizesvideobitratebyassociatingeachdronetoanoptimallymatchedserveranddynamicallyre-assignsdronesasrelaynodestoboostthethroughputinlow-throughputscenarios.Thisdynamicassignmentatcentralizedcontrolleroccursatslowertime-scalepermittedbyround-triplatencies,whilethepredictiveapproachanddroneslocaldecisionensuresthatthesystemworksinreal-time.Experimentalresultsovertensofflightsonthefieldsuggestoursystemcanachievereallygoodperformance,forexample,8dronescanachieveatradeoffof94%coverageand(onaverage)2Kvideosupportat20Mbpsbyoptimizingbetweencoverageandthroughput.Bydynamicallyallocatingdronestocoverthegameoractasrelays,oursystemalsodemonstratesa2xgainoversystemsmaximizingstaticcoveragealonethatachievesonly9Mbpsvideothroughput.

Chronus:ConsistentDataPlaneUpdatesinTimedSDNs JiaqiZheng(NanjingUniversity),GuihaiChen(NanjingUniversity),StefanSchmid(AalborgUniversity),HaipengDai(NanjingUniversity),JieWu(TempleUniversity)

Software-DefinedNetworks(SDNs)introduceinterestingnewopportunitiesinhownetworkroutescanbedefined,verified,andchangedovertime.Yetdespitethelogicallycentralizedperspectiveoffered,anSDNstillneedstobeconsideredadistributedsystem:ruleupdatescommunicatedfromthecontrollertotheindividualswitchestraverseanasynchronousnetworkandmayarriveout-of-order,andhenceleadto(temporaryorpermanent)inconsistencies.Accordingly,theconsistentnetworkupdateproblemhasrecentlyreceivedmuchattention.MotivatedbytheadventoftightlysynchronizedSDNs,weinthispaperinitiatethestudyofalgorithmsforconsistentnetworkupdatesintimedSDNsSDNsinwhichindividualnodeupdatescanbescheduledatspecifictimes.ThispaperpresentsChronus,whichisbasedonprovablycongestion-andloop-freeupdateschedulingalgorithms,andavoidstheflowtablespaceheadroomrequiredbyexistingtwophaseupdateapproaches.WeformulatetheMinimumUpdateTimeProblem(MUTP)asanoptimizationprogram.Weproposeatreealgorithmtocheckthefeasibilityandagreedyalgorithmtofindaupdatesequenceinpolynomialtime.ExtensiveexperimentsonMininetandnumericalsimulationsshowthatChronuscansubstantiallyreducetransientcongestionby75%andsaveover60%oftherulescomparedtothestateoftheart.

DistributedDeepNeuralNetworksovertheCloud,theEdgeandEndDevicesSuratTeerapittayanon(HarvardUniversity),BradleyMcDanel(HarvardUniversity),H.T.Kung(HarvardUniversity)

Weproposedistributeddeepneuralnetworks(DDNNs)overdistributedcomputinghierarchies,consistingofthecloud,theedge(fog)andenddevices.Whilebeingabletoaccommodateinferenceofadeepneuralnetwork(DNN)inthecloud,aDDNNalsoallowsfastandlocalizedinferenceusingshallowportionsoftheneuralnetworkattheedgeandenddevices.Moreover,viadistributedcomputing,DDNNsenhancedataprivacyandsystemfaulttoleranceforDNNapplications.Whensupportedbyascalabledistributedcomputinghierarchy,aDDNNcanscaleupinneuralnetworksizeandscaleoutingeographicalspan.InimplementingaDDNN,wemapsectionsofaDNNontoadistributedcomputinghierarchy.Byjointlytrainingthesesections,weminimizecommunicationandresourceusagefordevicesandmaximizeusefulnessofextractedfeatureswhichareutilizedinthecloud.Asaproofofconcept,weshowaDDNNcanexploitgeographicaldiversityofsensorstoimproverecognitionaccuracyandreducecommunicationcost.Inourexperiment,comparedwiththetraditionalmethodofoffloadingrawsensordatatobeprocessedinthecloud,DDNNlocallyprocessesmostsensordataonenddevicesandisabletoreducethecommunicationcostbyafactorofover20x.

Research6:DistributedGreenComputingandEnergyManagement

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DynamicControlofFlowCompletionTimeForPowerEfficientDataCenterNetworks KuangyuZheng(TheOhioStateUniversity),XiaoruiWang(TheOhioStateUniversity)

Datacenternetwork(DCN)canconsumeasignificantamountofpower(e.g.,10%to20%)inlarge-scaledatacenters.ToreducethepowerconsumptionofDCN,trafficconsolidationhasbeenrecentlyproposedasaneffectiveapproachtoreducethenumberofDCNdevicesinuse.However,existingconsolidationapproachesdonotsufficientlyconsidertheflowcompletiontime(FCT)requirement.Ononehand,missingtheFCTdeadlinescancauseseriousviolationofservice-levelagreement,especiallyfordelay-sensitivenetworkingservices,suchaswebsearchandE-commerce.Ontheotherhand,keepingallthedevicesontomakeFCTsmuchshorterthanthedesiredrequirementsisunnecessarybecause1)usersmaynotbeabletoperceivethedifference,and2)suchagreedystrategycanleadtounnecessarilyhighDCNpowerconsumptionandthusmoreelectricitycosts.Inthispaper,weproposeFCTcon,adynamicFCTcontrolstrategyforDCNpoweroptimization.FCTconisdesignedrigorouslybasedoncontroltheorytodynamicallycontroltheFCTofdelay-sensitivetrafficflowsexactlytorequirements,suchthatthedesiredFCTperformanceisguaranteedwhilethemaximumamountofDCNpowersavingscanbeachieved.Resultsfrombothhardwareexperimentsandsimulationevaluationsdemonstratethat,comparedtothestate-of-the-artDCNpoweroptimizationschemes,FCTconcanimprovetheDCNFCTperformance,whileachievingnearlythesameorevenmorepowersavings.Consequently,FCTconcanresultinmorethan22.0%to62.2%extranetprofitsforadatacenterwith50Kservers.

OnEnergy-EfficientCongestionControlforMultipathTCP JiaZhao(SimonFraserUniversity),JiangchuanLiu(SimonFraserUniversity),HaiyangWang(UniversityofMinnesotaDuluth)

Multipathtransportprotocols,e.g.MultipathTCP(MPTCP),enabletransmissionviamultipleroutesbetweenanend-to-endconnectiontoimproveresourceusageofregularTCP.Duetotheincreasingconcerningreencomputing,therehasbeensignificantinterestindesigningenergy-efficientmultipathtransport.ForexistingMPTCPcongestioncontrolalgorithms,theresearchcommunitystilllacksacomprehensiveunderstandingofwhichcomponentsinsuchanalgorithmplaythefundamentalroleinenergyefficiency,howvariousalgorithmscompareagainsteachotherfromenergy-consumingperspective,orwhetherthereexistpotentiallybettersolutionsforenergysaving.Inthispaper,wetakeafirststeptoanswerthesequestions.BasedontheMPTCPLinuxkernelexperiments,wefirstsummarizethattheenergyconsumptionisrelatedtothreeaspects:averagethroughput,pathdelayanddifferentnetworkscenarios.Inordertobridgecongestioncontroltothethreeaspects,weanalyzetheexistingalgorithmsandcapturetheessentialparametersofmultipathcongestioncontrolmodelrelatedtoMPTCPsenergyefficiency.Thenwedesignawindowincreasefactortoshifttraffictolow-delayenergy-efficientpaths.Wefurtherextendthisdesignbyusinganenergy-awarecompensativeparametertofitthegeneralhierarchicalInternettopology.Weevaluatetheperformanceofexistingmultipathcongestioncontrolalgorithmsandourproposedalgorithmindifferentnetworkscenarios.Theresultsshowenergyefficiencyofourdesign.

AMechanismforCooperativeDemand-SideManagement GuangchaoYuan(Microsoft),Chung-WeiHang(IBM),MichaelHuhns(UniversityofSouthCarolina),MunindarSingh(NorthCarolinaStateUniversity)

Demand-sidemanagement(DSM)isanimportantthemeintheSmartGridandoffersthepossibilityoflevelingpowerconsumptionwithitsattendantbenefitsofreducingcapitalexpenses.Thispaperdevelopsanalgorithmicmechanismthatreducespeaktotalconsumptionandencouragesprosocialbehavior,suchasexpressingflexibilityinonespowerconsumptionandreportingpreferencestruthfully.Ourobjectiveistoprovideatractable,budget-balancedmechanismthatpromotestruthtellingfromhouseholds.Theresultingmechanismistheoreticallyandempiricallyproventobeexantebudget-balanced,weaklyPareto-efficient,andweaklyBayesianincentive-compatible.Asimulationstudyverifiesthatthemechanismcouldlargelyreducethecomputationalcomplexitythattheoptimalallocationrequires,whilemaintainingapproximatelythesameperformance.Auserstudywith20subjectsfurthershowstheeffectivenessofthemechanisminpreventingparticipantsfromdefectingandincentivizingthemtorevealflexiblepreferences.

AHierarchicalFrameworkofCloudResourceAllocationandPowerManagementUsingDeepReinforcementLearning NingLiu(SyracuseUniversity),ZheLi(SyracuseUniversity),ZhiyuanXu(SyracuseUniversity),JielongXu(SyracuseUniversity),ShengLin(SyracuseUniversity),QinruQiu(SyracuseUniversity),JianTang(SyracuseUniversity),YanzhiWang(SyracuseUniversity)

Automaticdecision-makingapproaches,suchasreinforcementlearning(RL),havebeenappliedto(partially)solvetheresourceallocationproblemadaptivelyinthecloudcomputingsystem.However,acompletecloudresourceallocationframeworkexhibitshighdimensionsinstateandactionspaces,whichprohibittheusefulnessoftraditionalRLtechniques.Inaddition,highpowerconsumptionhasbecomeoneofthecriticalconcernsindesignandcontrolofcloudcomputingsystems,whichdegradessystemreliabilityandincreasescoolingcost.Aneffectivedynamicpowermanagement(DPM)policyshouldminimizepowerconsumptionwhilemaintainingperformancedegradationwithinanacceptablelevel.Thus,ajointvirtualmachine(VM)resourceallocationandpowermanagementframeworkarecriticaltotheoverallcloudcomputingsystem.Moreover,novelsolutionframeworkisnecessarytoaddresstheevenhigherdimensionsinstateandactionspaces.Inthispaper,weproposeanovelhierarchicalframeworkforsolvingtheoverallresourceallocationandpowermanagementproblemincloudcomputingsystems.TheproposedhierarchicalframeworkcomprisesaglobaltierforVMresourceallocationtotheserversandalocaltierfordistributedpowermanagementoflocalservers.Theemergingdeepreinforcementlearning(DRL)technique,whichcandealwithcomplicatedcontrolproblemswithlargestatespace,isadoptedtosolvetheglobaltierproblem.Furthermore,anautoencoderandanovelweightsharingstructureareadoptedtohandlethehigh-dimensionalstatespaceandacceleratetheconvergencespeed.Ontheotherhand,thelocaltierofdistributedserverpowermanagementscomprisesanLSTMbasedworkloadpredictorandamodel-freeRLbasedpowermanager,operatinginadistributedmanner.ExperimentresultsusingactualGoogleclustertracesshowthatourproposedhierarchicalframeworksignificantlysavesthepowerconsumptionandenergyusagethanthebaselinewhileachievingnoseverelatencydegradation.Meanwhile,theproposedframeworkcanachievethebesttrade-offbetweenlatencyandpower/energyconsumptioninaservercluster.

SunChase:Energy-EfficientRoutePlanningforSolar-PoweredEVs. LanduJiang(McGillUniversity),YuHua(HuazhongUniversityofScienceandTechnology),ChenMa(McGillUniversity),XueLiu(McGillUniversity)

Electricvehicles(EVs)playasignificantroleinthecurrenttransportationsystems.ThemainfactorthataffectsacceptanceofexistingEVmodelsistherangeanxietyproblemcausedbylimitedchargingstationsandlongrechargetimes.Recently,thesolar-poweredEVhasdrawnmanyattentionsduetobeingfreeofcharginglimitations.However,thesolarpoweredEVsmaystillstrugglewiththelimitedusebecauseofunpredictablesolaravailability.Forexample,shadingscausedbybuildingsandtreesalsopossiblydecreasethesolarpanelcellefficiency.Toaddressthis,weproposearouteplanningmethodforsolar-poweredEVstobalancetheenergyharvestingandconsumptionsubjecttotimeconstraint.Theideabehindoursolutionistoofferpower-awareoptimalrouting,whichmaximizestheon-roadenergyinputgivensolaravailabilityoneachroadsegment.Wefirstbuildasolaraccessestimationmodelusing3Dgeographicdataandthenemployamulti-criteria

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searchmethodtogenerateasetofParetocandidateroutes.Inordertoreducethesizeoftheset,weleveragethebisectkmeansclusteringalgorithmtoextractthemostrepresentativeParetorouteswithbettersolaravailability.Intheevaluation,wedevelopedavalidationplatformonthevehicleandleveragedmobilesensingtechniquestoexamineourproposedmodelinrealroadenvironments.Weconductedsimulationstoevaluateourproposedrouteplanningalgorithmusingreallifescenarios.Experimentalresultsdemonstratethatoursolarinputmodelisrobusttorealroadscenarios,andtheroutingalgorithmhasgreatpotentialtoprovideefficientservicesforsolar-poweredEVinthefuture.

Research7:InternetofThings,SmartCities,andCyber-PhysicalSystemsPersistentTrafficMeasurementThroughVehicle-to-InfrastructureCommunications HeHuang(SoochowUniversity),Yu-ESun(SoochowUniversity),ShigangChen(UniversityofFlorida),HongliXu(UniversityofScienceandTechnologyofChina),YianZhou(Google)

Measuringpointtrafficvolumeandpoint-to-pointtrafficvolumeinaroadsystemhasimportantapplicationsintransportationengineering.Theconnectedvehicletechnologiesintegratewirelesscommunicationsandcomputersintotransportationsystems,allowingwirelessdataexchangesbetweenvehiclesandroad-sideequipment,andenablinglarge-scale,sophisticatedtrafficmeasurement.Thispaperinvestigatestheproblemsofpersistentpointtrafficmeasurementandpersistentpoint-to-pointtrafficmeasurement,whichwerenotadequatelystudiedinthepriorart,particularlyinthecontextofintelligentvehicularnetworks.Weproposetwonovelestimatorsforprivacypreservingpersistenttrafficmeasurement:oneforpointtrafficandtheotherforpoint-to-pointtraffic.Theestimatorsaremathematicallyderivedfromthejoinresultoftrafficrecords,whichareproducedbytheelectronicroadsideunitswithprivacypreservingdatastructures.Weevaluateourestimationmethodsusingsimulationsbasedonbothrealtransportationtrafficdataandsyntheticdata.Thenumericalresultsdemonstratetheeffectivenessoftheproposedmethodsinproducinghighmeasurementaccuracyandallowingaccuracy-privacytradeoffthroughparametersetting.

TagBreathe:MonitorBreathingwithCommodityRFIDSystems YuxiaoHou(TheHongKongPolytechnicUniversity),YanwenWang(TheHongKongPolytechnicUniversity),YuanqingZheng(TheHongKongPolytechnicUniversity)

Breathmonitoringhelpsassessthegeneralpersonalhealthandgivescluestochronicdiseases.Yetcurrentbreathmonitoringtechnologiesareinconvenientandintrusive.Forinstance,typicalbreathmonitoringdevicesneedtoattachnasalprobesorchestbandstousers.Wirelesssensingtechnologieshavebeenappliedtomonitorbreathingusingradiowaveswithoutphysicalcontact.Thosewirelesssensingtechnologieshoweverrequirecustomizedradioswhicharenotreadilyavailable.Moreimportantly,duetointerference,suchtechnologiesdonotworkwellwithmultipleusers.Withmultipleusersinpresence,thedetectionaccuracyofexistingsystemsdecreasesdramatically.Inthispaper,weproposetomonitorusersbreathingusingcommercial-off-the-shelf(COTS)RFIDsystems.Inoursystem,passivelightweightRFIDtagsareattachedtousersclothesandbackscatterradiowaves,andcommodityRFIDreadersreportlowleveldata(e.g.,phasevalues).Wetrackperiodicbodymovementduetoinhalingandexhalingbyanalyzingthelowleveldatareportedbycommodityreaders.Toenhancethemeasurementrobustness,wesynthesizedatastreamsfromanarrayofmultipletagstoimprovethemonitoringaccuracy.OurdesignfollowsthestandardEPCprotocolwhicharbitratescollisionsinthepresenceofmultipletags.WeimplementaprototypethebreathmonitoringsystemwithcommodityRFIDsystems.Theexperimentresultsshowthattheprototypesystemcansimultaneouslymonitorbreathingwithhighaccuracyevenwiththepresenceofmultipleusers.

Double-EdgedSword:IncentivizedVerifiableProductPathQueryforRFID-enabledSupplyChain SaiyuQi(XidianUniversity),YuanqingZheng(TheHongKongPolytechnicUniversity),XiaofengChen(XidianUniversity),JianfengMa(XidianUniversity),YongQi(XianJiaotongUniversity)

Queryingthepathinformationofindividualproductsinasupplychainiskeytomanyapplications.RFID(RadioFrequencyIDentification)isamaintechnologytoenableproductpathinformationquerytoday.WithRFIDtechnology,supplychainparticipantscanefficientlytrackproductsintransitandrecordtheirstatesindatabases.Inthispaper,weinvestigatethefollowingquestion:howcanweconductprivacy-preservingproductpathinformationquerywithverifiabilityonanRFIDenableddistributedsupplychain?WeaddressthisquestionwithDoubleEdged(DE)-Sword,anincentivizedverifiablequerysystem.DE-Swordintroducesanoveldouble-edgedreputationincentivemechanismtoencouragesupplychainparticipantstobehave;andcouplesitwithcryptographicprimitivesandcarefulprotocoldesign.WeevaluateDE-Swordthroughsecurityanalysisandperformanceexperiments.ThesecurityanalysisshowsthatDE-Swordguaranteesbothverifiabilityandprivacy.TheexperimentresultsshowthatDE-SwordachieveslowoverheadinRFID-enabledsupplychainapplications.

TowardsAccurateCorruptionEstimationinZigBeeUnderCross-TechnologyInterference GongLongChen(ZhejiangUniversity),WeiDong(ZhejiangUniversity),ZhiweiZhao(UniversityofElectronicScienceandTechnologyofChina),TaoGu(RMITUniversity)

Cross-TechnologyInterferenceaffectstheoperationoflow-powerZigBeenetworks,especiallyundersevereWiFiinterference.AccuratecorruptionestimationisveryimportanttoimprovetheresilienceofZigBeetransmissions.However,therearemanylimitationsinexistingapproachessuchaslowaccuracy,highoverhead,andrequiringhardwaremodification.Inthispaper,weproposeanaccuratecorruptionestimationapproach,AccuEst,whichutilizesper-byteSINR(Signal-to-Interferenceand-NoiseRatio)todetectcorruption.Wecombinetheuseofpilotsymbolswithper-byteSINRtoimprovecorruptiondetectionaccuracy,especiallyinhighlynoisyenvironments(i.e.,noiseandinterferenceareatthesamelevel).Inaddition,wedesignanadaptivepilotinstrumentationschemetostrikeagoodbalancebetweenaccuracyandoverhead.WeimplementAccuEstontheTinyOS2.1.1/TelosBplatformandevaluateitsperformancethroughextensiveexperiments.ResultsshowthatAccuEstimprovescorruptiondetectionaccuracyby78.6%onaveragecomparedwithstate-of-the-artapproach(i.e.,CARE)inhighlynoisyenvironments.Inaddition,AccuEstreducespilotoverheadby53.7%onaveragecomparedtothetraditionalpilot-basedapproach.WeimplementAccuEstinacoding-basedtransmissionprotocol,andresultsshowthatwithAccuEst,thepacketdeliveryratioisimprovedby20.3%onaverage.

UnseenActivityRecognition:AHierarchicalActiveTransferLearningApproach MohammadArifUlAlam(UniversityofMarylandBaltimoreCounty),NirmalyaRoy(UniversityofMarylandBaltimoreCounty)

Humanactivityrecognition(AR)isanessentialelementforuser-centricandcontext-awareapplications.Whilepreviousstudiesshowedpromisingresultsusingvariousmachinelearningalgorithms,mostofthemcanonlyrecognizetheactivitiesthatwerepreviouslyseeninthetrainingdata.Weinvestigatethechallengesofimprovingtherecognitionofunseendailyactivitiesinsmarthomeenvironment,bybetterexploitingthehierarchicaltaxonomyofcomplexdailyactivities.Wefirst(a)designahierarchicalrepresentationofcomplexactivitytaxonomyintermsofhuman-readablesemanticattributes,and(b)developahierarchyofclassifierswhichincorporatesaclustertreebuiltonthedomainknowledgefromtrainingsamples.Thoughthismodelisrichinrecognizingcomplexactivitiesthatare

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previouslyseenintrainingdata,itisnotwellversedtorecognizeunseencomplexactivitieswithoutnewtrainingsamples.Totacklethischallenge,weextendHierarchicalActiveTransferLearning(HATL)approachthatexploitssemanticattributeclusterstructureofcomplexactivitiessharedbetweenseen(source)andunseen(target)activitydomains.Ourapproachemploystransferandactivelearningtohelplabeltargetdomainunlabeleddatabyspawningthemosteffectivequeries.Weevaluatedourapproachwithtworeal-timesmarthomesystems(IRB#HP-00064387)whichcorroboratesradicalimprovementsinrecognizingunseencomplexactivities.

RFIPad:EnablingCost-efficientandDevice-freeIn-airHandwritingusingPassiveTags HanDing(Xi'anJiaotongUniversity),ChenQian(UniversityofCaliforniaSantaCruz),JinsongHan(Xi'anJiaotongUniversity),GeWang(Xi'anJiaotongUniversity),WeiXi(Xi'anJiaotongUniversity),KunZhao(Xi'anJiaotongUniversity),JizhongZhao(Xi'anJiaotongUniversity)

Animportantfunctionofsmartenvironmentsistheubiquitousaccessofcomputingdevices.Inpublicareassuchashospitals,libraries,andairports,peoplemaywanttointeractwithnearbycomputingsystemstogetinformation,suchasdirectionstoahospitalroom,locationsofbooks,andflightdeparture/arrivalinformation.Touchscreenbaseddisplaysandkiosks,whicharecommonlyusedtoday,mayincurextrahardwarecostorevenpossiblegermandbacteriainfection.Thisworkprovidesanewsolution:userscanmakequeriesandinputsbyperformingin-airhandwritingtoanarrayofpassiveRFIDtags,namedRFIPad.Thisinputmethoddoesnotrequirehumanhandstocarryanydeviceandhenceisconvenientforapplicationsinpublicareas.Besidesthemobileandcontactlessproperty,thissystemisacost-efficientextensiontocurrentRFIDsystems:anexistingreadercanmonitormultipleRFIPadswhileperformingitsregularapplicationssuchasidentificationandtracking.WeimplementaprototypeofRFIPadusingcommercialoff-the-shelfUHFRFIDdevices.ExperimentalresultsshowthatRFIPadachieves>91%accuracyinrecognizingbasictouchscreenoperationsandEnglishletters.

Research8:MobileandWirelessComputingSystemsIRobustIncentiveTreeDesignforMobileCrowdsensing XiangZhang(ArizonaStateUniversity),GuoliangXue(ArizonaStateUniversity),RuozhouYu(ArizonaStateUniversity),DejunYang(ColoradoSchoolofMines),JianTang(SyracuseUniversity)

Withtheproliferationofsmartmobiledevicessuchassmartphones,tablets,andwearable,mobilecrowdsensingbecomesapowerfulsensingandcomputationparadigmwhichhasbeenappliedinmanyfields,suchasspectrumsensing,environmentalmonitoring,healthcare,andsoon.Drivenbypromisingincentives,thepowerofthecrowdgrantscrowdsensinganadvantageinmobilizinguserswhoperformsensingtaskswiththeembeddedsensorsonthesmartdevices.Auctionisoneofthecommonlyadoptedcrowdsensingincentivemechanismstoincentivizeusersforparticipation.However,auctiondoesnotconsidertheincentiveforusersolicitationwhereincrowdsensing,alargenumberofusersisoftenneeded.Todealwiththisissue,weaimtodesignanauction-basedincentivetreetoofferrewardstousersforbothparticipationandsolicitation.Meanwhile,wewanttheincentivemechanismtoberobustagainstdishonestbehaviorsuchasuntruthfulbiddingandsybilattacks,toeliminatethemaliciouspricemanipulation.WedesignanincentivemechanismRIT,whichcombinestheadvantagesofauctionsandincentivetrees.WeprovethatRITistruthfulandsybil-proofwithprobabilityatleastH,foranygivenH_(0,1).WealsoprovethatRITsatisfiesindividualrationality,computationalefficiency,andsolicitationincentive.SimulationresultsofRITfurtherconfirmouranalysis.

WearLock:UnlockingYourPhoneviaAcousticsusingSmartwatch ShanheYi(CollegeofWilliamandMary),ZhengruiQin(NorthwestMissouriStateUniversity),NancyCarter(CollegeofWilliamandMary),QunLi(CollegeofWilliamandMary)

Smartphonelockscreensareimplementedtoreducetheriskofdatalossorcompromisegiventhefactthatincreasingamountofpersondataareaccessibleonsmartphonesnowadays.Unfortunately,manysmartphoneusersabandonlockscreensduetotheinconvenienceofunlockingtheirphonesmanytimesaday.Withthewideadoptionofwearables,token-basedapproacheshavegainedpopularityinsimplifyingunlockingandretainingsecurityatthesametime.Tothisend,weproposetotakeadvantageofthesmartwatchforeasysmartphoneunlocking.Inthispaper,wehavedesignedWearLock,asystemthatusesacoustictonesastokenstoautomatetheunlockingsecurely.Webuildasub-channelselectionandanadaptivemodulationintheacousticmodemtomaximizeunlockingsuccessrateagainstambientnoiseonlywhenthosetwodevicesarenearby.Weleveragethemotionsensoronthesmartwatchtoreducetheunlockfrequency.Weoffloadsmartwatchtaskstothesmartphonetospeedupcomputationandsaveenergy.WehaveimplementedtheWearLockprototypeandconductedextensiveevaluations.Resultsachievedalowaveragebiterrorrate(BER)as8%invariousexperiments.Comparedtotraditionalmanualpersonalidentificationnumbers(PINs)entry,WearLockachievesatleast18%unlockspeedupwithoutanymanualeffort.

ModelingMobileCodeAccelerationintheCloud HuberFlores(UniversityofOulu),XiangSu(UniversityofOulu),VassilisKostakos(UniversityofOulu),JukkaRiekki(UniversityofOulu),EemilLagerspetz(UniversityofHelsinki),SasuTarkoma(HelsinkiUniversityofTechnology),PanHui(HKUST),YongLi(TsinghuaUniversity),JukkaManner(AaltoUniversity)

Tuningthequalityofserviceofamobileapplicationiscriticalinordertoensureuserssatisfaction.Techniqueshavebeenproposedtoaccomplishadaptationofqualityofservicedynamically.However,thereisstillalimitedunderstandingabouthowtoprovideanutilitymodelforcodeexecution.Onekeychallengeistomodelthelevelofqualityinthecodeexecutionthatcanbeprovisionedbythecloud.Sincetheallocationofcloudresourceshasacost,itisimportanttooptimizecloudusage.Weproposeasoftware-definednetworkingapproachthatallowsmodelingandcontrollingcodeaccelerationofamobileapplicationdeployedacrossmultipletypeofdevices.Bysegregatingthecomputationalrequirementsofthemobileapplicationintogroups,wewereabletodefinetheaccelerationneededbyeachgroupofdevices.Asthecomputationalrequirementsofadevicecanchangeacrosstime,amobiledevicecanbere-assignedtoanothergroupbasedondemand.OurSDNapproachimplementsamodelthatallowsthesystemtopredictworkloadbasedonaccelerationgroups.Evaluatingoursysteminarealtestbedshowedthatitispossibletopredictworkloadandallocateoptimalresourcestohandlethatworkloadwith87.5%ofaccuracy.

E-Android:ANewEnergyProfilingToolforSmartphones XingGao(CollegeofWIlliamandMary),DachuanLiu(CollegeofWIlliamandMary),DaipingLiu(UniversityofDelaware),HainingWang(UniversityofDelaware),AngelosStavrou(GeorgeMasonUniversity)

Smartphoneshavebecomeanindispensablepartofourdailylives.Asthelimitedbatterylifetimeremainsamajorfactorrestrictingtheapplicabilityofasmartphone,significantresearcheffortshavebeendevotedtounderstandtheenergyconsumptioninsmartphones.Existingenergymodelingmethodscanaccountenergydraininafine-grainedmannerandprovidewelldesignedhuman-batteryinterfacesforuserstocharacterizeenergyusageofeveryappinsmartphones.

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However,inthispaper,wedemonstratethattherearestillpitfallsincurrentAndroidenergymodelingapproaches,makingAndroidvulnerabletomaliciousattacks.Inparticular,wepresentasetofnewcollateralenergyattacks,whichcandepletebatterylifebutsidestepthesupervisionofcurrentenergyaccounting.Todefendagainstcollateralenergyattacks,weproposeE-Androidtoaccuratelyprofileenergyconsumptionofasmartphoneinacomprehensivemanner.E-Androidmonitorscollateralenergyrelatedeventsandmaintainsenergyconsumptionmapsforrelevantapps.WeevaluatetheeffectivenessofE-Androidundersixdifferentcollateralenergyattacksandtwonormalscenarios,andcomparetheresultswiththoseofAndroid.WhileAndroidfailstodiscloseallcollateralenergyattacks,E-Androidcanaccuratelyprofileenergyconsumptionandrevealtheexistenceofenergymalware.OurevaluationresultsalsoshowthattheoverheadinducedbyE-Androidisminor.

LocalandLow-CostWhiteSpaceDetection AhmedSaeed(GeorgiaInstituteofTechnology),KhaledHarras(CarnegieMellonUniversity),EllenZegura(GeorgiaInstituteofTechnology),MostafaAmmar(GeorgiaInstituteofTechnology)

WhitespacesareportionsoftheTVspectrumthatareallocatedbutnotusedlocally.Ifaccuratelydetected,whitespacesofferavaluablenewopportunityforhighspeedwirelesscommunications.Weproposeanewmethodforwhitespacedetectionthatallowsanodetoactlocally,basedonacentrallyconstructedmodel,andatlowcost,whiledetectingmorespectrumopportunitiesthanbestknownapproaches.Weleveragetwoideas:first,wedemonstratethatlow-costspectrummonitoringhardwarecanoffergoodenoughdetectioncapabilities.Second,wedevelopamodelthatcombineslocallymeasuredsignalfeaturesandlocationtomoreefficientlydetectwhitespaceavailability.Weincorporatetheseideasintothedesign,implementation,andevaluationofacompletesystemwecallWaldo.WedeployWaldoonlaptopinAtlantametropolitanareaintheUScovering700km2showingthatusingsignalfeaturesinadditiontolocationcanimprovedetectionaccuracybyupto10xforsomechannels.WealsodeployWaldoonanAndroidsmartphone,demonstratingthefeasibilityofreal-timewhitespacedetectionwithefficientuseofsmartphoneresources.

GeneralAnalysisofIncentiveMechanismsforPeer-to-PeerTransmissions:AQuantumGamePerspective WeimanSun(BeijingNormalUniversity),ShenglingWang(BeijingNormalUniversity)

Thepeer-to-peertransmissionisamainstreaminchallengednetworkenvironments.Yet,thefreeriderphenomenoninpeer-topeertransmissionspressesaneedforincentivemechanismstostimulatecontributionsofdatatransmission.Asaresult,itisimperativetoanswerthequestions:whetherandtowhatextentanincentivemechanismcaninvokesuchcontributions?Toanswerthesequestions,weemployann-playercontinuousquantumgamemodeltoanalyzeextrinsicincentivemechanisms(promotingcooperativebehaviorsbyofferingrewards),andusethequantumprisoner’sdilemmamodeltoanalyzeintrinsicincentivemechanisms(encouragingreciprocalcooperationbyexploitinginternalbounds).Tothebestofourknowledge,wearethefirsttoanalyzeincentivemechanismsforpeer-to-peertransmissionsfromaquantumgameperspective.Suchaperspectiveisadoptedbecausetheextendedstrategyspaceinthequantumgamebroadenstherangeforsearchingoptimalstrategiesandtheintroductionofentanglementmakestheproposedanalyticalframeworksmorepracticalduetotheconsiderationofthepeers’relationshipsindecision-making.Ourproposedquantumgame-basedanalyticalframeworksaregenericbecausetheyarecompatiblewithclassicgame-basedschemes.Ouranalyticalresultscanprovidestraightforwardinsightsonevaluatingthepotentialofincentivemechanismsandcanserveasimportantreferencesfordesigningnewincentivemechanisms.

Research9:DistributedBigDataSystemsHigh-PerformanceandResilientKey-ValueStorewithOnlineErasureCodingforBigDataWorkloads DiptiShankar(TheOhioStateUniversity),XiaoyiLu(TheOhioStateUniversity),DhabaleswarPanda(TheOhioStateUniversity)

Distributedkey-valuestore-basedcachingsolutionsarebeingincreasinglyusedtoaccelerateBigDataapplicationsonmodernHPCclusters.Thishasnecessitatedincorporatingfaulttolerancecapabilitiesintohigh-performancekey-valuestoressuchasMemcachedthatareotherwisevolatileinnature.Inmemoryreplicationisbeingusedastheprimarymechanismtoensureresilientdataoperations.However,thisincursincreasednetworkI/Owithhighremotememoryrequirements.Ontheotherhand,erasurecodingisbeingextensivelyexploredforenablingdataresilience,whileachievingbetterstorageefficiency.Inthispaper,wefirstperformanin-depthmodeling-basedanalysisoftheperformancetrade-offsofIn-MemoryReplicationandErasureCodingschemesforkey-valuestores,andexplorethepossibilitiesofemployingOnlineErasureCodingforenablingresilienceinhigh-performancekey-valuestoresforHPCclusters.Wethendesignanon-blockingAPI-basedenginetoperformefficientSet/Getoperationsbyoverlappingtheencoding/decodinginvolvedinenablingErasureCoding-basedresiliencewiththerequest/responsephases,byleveragingRDMAonhighperformanceinterconnects.PerformanceevaluationsshowthattheproposeddesignscanoutperformsynchronousRDMA-basedreplicationbyabout2.8x,andcanimproveYCSBthroughputandaverageread/writelatenciesbyabout1.34x-2.6xoverasynchronousreplicationforlargerkey-valuepairsizes(>16KB).Wealsodemonstrateitsbenefitsbyincorporatingitintoahybridandresilientkey-valuestore-basedburst-buffersystemoverLustreforacceleratingBigDataI/OonHPCclusters.

ModelingandAnalyzingLatencyintheMemcachedsystemWenxueCheng(TsinghuaUniversity),FengyuanRen(TsinghuaUniversity),WanchunJiang(CentralSouthUniversity),TongZhang(TsinghuaUniversity)

Memcachedisawidelyusedin-memorycachingsolutioninlarge-scalesearchingscenarios.ThemostpivotalperformancemetricinMemcachedislatency,whichisaffectedbyvariousfactorsincludingtheworkloadpattern,theservicerate,theunbalancedloaddistributionandthecachemissratio.Toquantitatetheimpactofeachfactoronlatency,weestablishatheoreticalmodelfortheMemcachedsystem.Specially,weformulatetheunbalancedloaddistributionamongMemcachedserversbyasetofprobabilities,capturetheburstandconcurrentkeyarrivalsatMemcachedserversinformofbatchingblocks,andaddacachemissprocessingstage.Basedonthismodel,algebraicderivationsareconductedtoestimatelatencyinMemcached.Thelatencyestimationisvalidatedbyintensiveexperiments.Moreover,weobtainaquantitativeunderstandingofhowmuchimprovementoflatencyperformancecanbeachievedbyoptimizingeachfactorandprovideseveralusefulrecommendationstooptimallatencyinMemcached.

SpeculativeSlotReservation:EnforcingServiceIsolationforDependentData-ParallelComputations ChenChen(HKUST),WeiWang(HKUST),BoLi(HKUST)

Priorityschedulingisafundamentaltooltoprovideserviceisolationfordifferentjobsinsharedclusters.Ideally,theperformanceofahigh-priorityjobshouldnotbedraggeddownbyanotherwithalowerpriority.However,weshowinthispaperthatsimplyassigningahighpriorityprovidesnoisolationforjobswithdependentcomputations.Ajob,evenreceivingthehighestpriority,maygiveupcomputeslotstoanotherbeforeproceedingtothedownstreamcomputation,whichisbecause

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ofbarrier,i.e.,thatthedownstreamcomputationcannotstartuntilalltheupstreamtaskshavecompleted.Suchaninterruptionofexecutioninevitablyresultsinasignificantdelay.Inthispaper,weproposespeculativeslotreservationthatjudiciouslyreservesslotsfordownstreamcomputations,soastoretainserviceisolationforhigh-priorityjobs.Tomitigatetheutilizationlossduetoslotreservation,weanalyzethetrade-offbetweenutilizationandisolation,andexposeatunableknobtonavigatethetrade-off.Wealsoproposeacomplementarystragglermitigationstrategythatusesthereservedslotstorunextracopiesofslowtasks.WehaveimplementedspeculativeslotreservationinSpark.Evaluationsbasedonbothclusterdeploymentandtrace-drivensimulationsshowthatourapproachenforcesstrictserviceisolationforhigh-priorityjobs,withoutslowingdowntheotherjobswithalowerpriority.

OptimizingShuffleinWide-AreaDataAnalytics ShuhaoLiu(UniversityofToronto),HaoWang(UniversityofToronto),BaochunLi(UniversityofToronto)

Asincreasinglylargevolumesofrawdataaregeneratedatgeographicallydistributeddatacenters,theyneedtobeefficientlyprocessedbydataanalyticjobsspanningmultipledatacentersacrosswide-areanetworks.Designedforasingledatacenter,existingdataprocessingframeworks,suchasApacheSpark,arenotabletodeliversatisfactoryperformancewhenthesewide-areaanalyticjobsareexecuted.Aswide-areanetworksinterconnectingdatacentersmaynotbecongestionfree,thereisacompellingneedforanewsystemframeworkthatisoptimizedforwide-areadataanalytics.Inthispaper,wedesignandimplementanewproactivedataaggregationframeworkbasedonApacheSpark,withafocusonoptimizingthenetworktrafficincurredinshufflestagesofdataanalyticjobs.Theobjectiveofthisframeworkistostrategicallyandproactivelyaggregatetheoutputdataofmappertaskstoasubsetofworkerdatacenters,asareplacementtoSparksoriginalpassivefetchmechanismacrossdatacenters.Itimprovestheperformanceofwide-areaanalyticjobsbyavoidingrepetitivedatatransfers,whichimprovestheutilizationofinter-datacenterlinks.OurextensiveexperimentalresultsusingstandardbenchmarksacrosssixAmazonEC2regionshaveshownthatourproposedframeworkisabletoreducejobcompletiontimesbyupto73%,ascomparedtotheexistingbaselineimplementationinSpark.

JobSchedulingwithoutPriorInformationinBigDataProcessingSystems ZhimingHu(UniversityofToronto),BaochunLi(UniversityofToronto),ZhengQin(InstituteofHighPerformanceComputing),RickSiowMongGoh(InstituteofHighPerformanceComputing)

Jobschedulingplaysanimportantroleforimprovingtheoverallsystemperformanceinbigdataprocessingframeworks.Simplejobschedulingpolicies,suchasFairandFIFOschedulingdonotconsiderjobsizes,andmaydegradetheperformancewhenjobsofvaryingsizesarrive.Moreelaboratejobschedulingpoliciesmaketheconvenientassumptionthatjobsarerecurring,andcompleteinformationabouttheirsizesisavailablefromtheirpriorruns.Inthispaper,wedesignandimplementanefficientandpracticaljobschedulerforbigdataprocessingsystemstoachievebetterperformanceevenwithoutpriorinformationaboutjobsizes.Thesuperiorperformanceofourjobscheduleroriginatesfromthedesignofamultiplelevelpriorityqueue,wherejobsaredemotedtolowerpriorityqueuesiftheamountofserviceconsumedsofarreachesacertainthreshold.Inthiscase,jobsinneedofasmallamountofservicecanfinishinthetopmostseverallevelsofqueues,whilejobsthatneedalargeamountofservicetocompletearemovedtolowerpriorityqueuestoavoidhead-of-lineblocking.Ournewjobschedulercaneffectivelymimicashortestjobfirstschedulingpolicywithoutknowingthejobsizesinadvance.Todemonstrateitsperformance,wehaveimplementedournewjobschedulerinYARN,apopularresourcemanagerusedbyHadoop/Spark,andvalidateditsperformancewithbothexperimentsonrealdatasetsandlarge-scaletrace-drivensimulations.Ourexperimentalandsimulationresultshavestronglyconfirmedtheeffectivenessofourdesign:ournewjobschedulercanreducetheaveragejobresponsetimeoftheFairschedulerbyupto45%.

DistributedLoadBalancinginKey-ValueNetworkedCachesSikderHuq(TheUniversityofIowa),ZubairShafiq(TheUniversityofIowa),SukumarGhosh(TheUniversityofIowa),AmirKhakpour(VerizonDigitalMediaServices),HarkeeratBedi(VerizonDigitalMediaServices)

Modernwebservicesrelyonanetworkofdistributedcacheserverstoefficientlydelivercontenttousers.Loadimbalanceamongcacheserverscansubstantiallydegradecontentdeliveryperformance.Duetotheskewedanddynamicnatureofreal-worldworkloads,cacheserversthatserveviralcontentexperiencehigherloadascomparedtoothercacheservers.WeproposeanoveldistributedloadbalancingprotocolcalledMeezantoaddresstheloadimbalanceamongcacheservers.Meezanreplicatespopularobjectstomitigateskewnessandadjustshashspaceboundariesinresponsetoloaddynamicsinanovelway.OurtheoreticalanalysisshowsthatMeezanachievesnearperfectloadbalancingforawiderangeofoperatingparameters.OurtracedrivensimulationsshowsthatMeezanreducesloadimbalancebyupto52%ascomparedtopriorsolutions.

Research10:DistributedAlgorithmsandTheoryICognitiveContext-awareDistributedStorageOptimizationinMobileCloudComputing:AStableMatchingbasedApproach DongHan(OaklandUniversity),YeYan(OaklandUniversity),TaoShu(AuburnUniversity),LiuqingYang(ColoradoStateUniversity),ShuguangCui(UniversityofCalifornia,Davis)

Mobilecloudstorage(MCS)isbeingextensivelyusednowadaystoprovidedataaccessservicestovariousmobileplatformssuchassmartphonesandtablets.Forcross-platformmobileapps,MCSisafoundationforsharingandaccessinguserdataaswellassupportingseamlessuserexperienceinamobilecloudcomputingenvironment.However,themobileusageofsmartphonesortabletsisquitedifferentfromlegacydesktopcomputers,inthesensethateachuserhashis/herownmobileusagepattern.Therefore,itischallengingtodesignanefficientMCSthatisoptimizedforindividualusers.Inthispaper,weinvestigateadistributedMCSsystemwhoseperformanceisoptimizedbyexploitingthefine-grainedcontextinformationofeverymobileuser.Inthisdistributedsystem,lightweightstorageserversaredeployedpervasively,suchthatdatacanbestoredclosertoitsuser.WesystematicallyoptimizethedataaccessefficiencyofsuchadistributedMCSbyexploitingthreetypesofusercontextinformation:mobilitypattern,networkcondition,anddataaccesspattern.Weproposetwooptimizationformulations:acentralizedonebasedonmixed-integerlinearprogramming(MILP),andadistributedonebasedonstablematching.Wethendevelopsolutionstobothformulations.Comprehensivesimulationsareperformedtoevaluatetheeffectivenessoftheproposedsolutionsbycomparingthemagainsttheircounterpartsundervariousnetworkandcontextconditions.

FairCachingAlgorithmsforPeerDataSharinginPervasiveEdgeComputingEnvironments YaodongHuang(StonyBrookUniversity),XintongSong(PekingUniversity),FanYe(StonyBrookUniversity),YuanyuanYang(StonyBrookUniversity),XiaomingLi(PekingUniversity)

Edgedevices(e.g.,smartphones,tablets,connectedvehicles,IoTnodes)withsensing,storageandcommunicationresourcesareincreasinglypenetratingourenvironments.Manynovelapplicationscanbecreatedwhennearbypeeredgedevicessharedata.Cachingcangreatlyimprovethedataavailability,retrieval

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robustnessandlatency.Inthispaper,westudytheuniqueissueofcachingfairnessinedgeenvironment.Duetodistinctownershipofpeerdevices,cachingloadbalanceiscritical.Weconsiderfairnessmetricsandformulateanintegerlinearprogrammingproblem,whichisshownassummationofmultipleConnectedFacilityLocation(ConFL)problems.WeproposeanapproximationalgorithmleveraginganexistingConFLapproximationalgorithm,andprovethatitpreservesa6.55approximationratio.Wefurtherdevelopadistributedalgorithmwheredevicesexchangedatareachabilityandidentifypopulatecandidatesascachingnodes.Extensiveevaluationshowsthatcomparedwithexistingwirelessnetworkcachingalgorithms,ouralgorithmsimprovesthe75-percentilefairnessfrom22.8%to71.4%,whileachievingcontentionthuslatencysimilarasthebestexistingworks.

Latency-DrivenCooperativeTaskComputinginMulti-UserFog-RadioAccessNetworks Ai-ChunPang(NationalTaiwanUniversity),Wei-HoChung(AcademiaSinica),Te-ChuanChiu(NationalTaiwanUniversity),JunshanZhang(ArizonaStateUniversity)

Fogcomputingisemergingasonepromisingsolutiontomeettheincreasingdemandforultra-lowlatencyservicesinwirelessnetworks.Takingaforward-lookingperspective,weproposeaFog-RadioAccessNetwork(F-RAN)model,whichutilizestheexistinginfrastructure,e.g.,smallcellsandmacrobasestations,toachievetheultra-lowlatencybyjointcomputingacrossmultipleF-RANnodesandnear-rangecommunicationsattheedge.Wetreatthelowlatencydesignasanoptimizationproblem,whichcharacterizesthetradeoffbetweencommunicationandcomputingacrossmultipleF-RANnodes.SincethisproblemisNP-hard,weproposealatency-drivencooperativetaskcomputingalgorithmwithone-for-allconceptforsimultaneousselectionoftheF-RANnodestoservewithproperheterogeneousresourceallocationformulti-userservices.Consideringthelimitedheterogeneousresourcessharedamongallusers,weadvocatetheone-for-allstrategyforeveryusertakingotherssituationintoconsiderationandseekforawinwinsolution.Thenumericalresultsshowthatthelow-latencyservicescanbeachievedbyF-RANvialatency-drivencooperativetaskcomputing.

ApproximationandOnlineAlgorithmsforNFV-EnabledMulticastinginSDNs ZichuanXu(UniversityCollegeLondon),WeifaLiang(TheAustralianNationalUniversity),MeitianHuang(TheAustralianNationalUniversity),MikeJia(TheAustralianNationalUniversity),SongGuo(TheHongKongPolytechnicUniversity),AlexGalis(UniversityCollegeLondon)

Multicastingisafundamentalfunctionalityofnetworksformanyapplicationsincludingonlineconferencing,eventmonitoring,videostreaming,andsystemmonitoringindatacenters.Toensuremulticastingreliable,secureandscalable,aservicechainconsistingofnetworkfunctions(e.g.,firewalls,IntrusionDetectionSystems(IDSs),andtranscoders)usuallyisassociatedwitheachmulticastrequest.SuchamulticastrequestisreferredtoasanNFV-enabledmulticastrequest.InthispaperwestudyNFV-enabledmulticastinginaSoftware-DefinedNetwork(SDN)withtheaimstominimizetheimplementationcostofeachNFV-enabledmulticastrequestormaximizethenetworkthroughputforasequenceofNFV-enabledrequests,subjecttonetworkresourcecapacityconstraints.WefirstformulatenovelNFV-enabledmulticastingandonlineNFV-enabledmulticastingproblems.Wethendevisetheveryfirstapproximationalgorithmwithanapproximationratioof2KfortheNFV-enabledmulticastingproblemifthenumberofserversforimplementingthenetworkfunctionsofeachrequestisnomorethanaconstantK(1).WealsostudydynamicadmissionsofNFV-enabledmulticastrequestswithouttheknowledgeoffuturerequestarrivalswiththeobjectivetomaximizethenetworkthroughput,forwhichweproposeanonlinealgorithmwithacompetitiveratioofO(logn)whenK=1,wherenisthenumberofnodesinthenetwork.Wefinallyevaluatetheperformanceoftheproposedalgorithmsthroughexperimentalsimulations.Experimentalresultsdemonstratethattheproposedalgorithmsoutperformotherexistingheuristics.

DistributedAuctionsforTaskAssignmentandSchedulinginMobileCrowdsensingSystems ZhuojunDuan(GeorgiaStateUniversity),WeiLi(GeorgiaStateUniversity),ZhipengCai(GeorgiaStateUniversity)

WiththeemergenceofMobileCrowdsensingSystems(MCSs),manyauctionschemeshavebeenproposedtoincentivizemobileuserstoparticipateinsensingactivities.However,inmostoftheexistingwork,theheterogeneityofMCSshasnotbeenfullyexploited.Totacklethisissue,inthispaper,westudythejointproblemofsensingtaskassignmentandschedulingwhileconsideringpartialfulfillment,attributediversity,andpricediversity.Wefirstelaboratelymodeltheproblemasareverseauctionanddesignadistributedauctionframework.Then,basedonthisframework,weproposetwodistributedauctionschemes,costpreferredauctionscheme(CPAS)andtimeschedule-preferredauctionscheme(TPAS),whichdifferonthemethodsoftaskscheduling,winnerdetermination,andpaymentcomputation.WefurtherrigorouslyprovethatbothCPASandTPAScanachievecomputational-efficiency,individual-rationality,budgetbalance,andtruthfulness.Finally,thesimulationresultsvalidatetheeffectivenessofbothCPASandTPASintermsofsensingtasksallocationefficiency,mobileusersworkingtimeutilizationandutility,andtruthfulness.

EffectiveMobileDataTradinginSecondaryAd-hocMarketwithHeterogeneousandDynamicEnvironment HengkySusanto(HuaweiFutureNetworkTheoryLab),HonggangZhang(UniversityofMassachusettsBoston),ShingYipHo(ShareMedia),BenyuanLiu(UniversityofMassachusettsLowell)

Advancesinsmartphonetechnologiesenablemobiledatasubscriberstoreselltheirdataallowancetootherusers,creatingasecondarydatamarket.Thetradingenvironmentofthissecondarydatamarketisdynamicandad-hoc:buyersandsellersjoinandleavethemarketatalltimes,changingthetradinglandscapeconstantly.Theamountofdatademandedandofferedatanypointintimealsovary.Theseconditionsmakedeterminingafairtransactionprice,andmatchingbuyerstosellersdifficultinpractice.Priorschemesutilizeglobaldescriptionofthenetworkandmarketforcestoachievegoodperformance,buttheimplementationrequiresahighoverheadcost.Inthispaper,wepresentDataMart,adatapricingandusermatchingplatformfortradinginthisdynamic,ad-hocandheterogeneousmarketthatworksindistributedmannerwithoutneedingglobalinformation.Usinginsightsfromrealworldtraces,wedemonstrateviasimulationthatourpricingschemeisconvergingandconsistentwiththelawofdemandandsupply.Further,ourusermatchingschemeachievescomparableperformancetotheoptimalsolution.WeimplementaprototypeonAndroidplatform,andtheexperimentresultsconfirmtheeffectivenessofDataMart.

Research11:SecurityandPrivacyinDistributedSystemsIIKalis-ASystemforKnowledge-drivenAdaptableIntrusionDetectionfortheInternetofThings DanieleMidi(PurdueUniversity),AntoninoRullo(UniversityofCalabria),AnandMudgerikar(PurdueUniversity),ElisaBertino(PurdueUniversity)

Inthispaper,weintroduceKalis,aself-adapting,knowledge-drivenexpertIntrusionDetectionSystemabletodetectattacksinrealtimeacrossawiderangeofIoTsystems.KalisdoesnotrequirechangestoexistingIoTsoftware,canmonitorawidevarietyofprotocols,hasnoperformanceimpactonapplicationsonIoTdevices,andenablescollaborativesecurityscenarios.KalisisthefirstcomprehensiveapproachtointrusiondetectionforIoTthatdoesnottargetindividualprotocolsor

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applications,andadaptsthedetectionstrategytothespecificnetworkfeatures.ExtensiveevaluationshowsthatKalisiseffectiveandefficientindetectingattackstoIoTsystems.

FuzzyExtractorsforBiometricIdentification NanLi(CSIRO),FuchunGuo(UniversityofWollongong),YiMu(UniversityofWollongong),WillySusilo(UniversityofWollongong),SuryaNepal(CSIRO)

Fuzzyextractorprovideskeygenerationfrombiometricsandothernoisydata.Thegeneratedkeyisseamlesslyusableforanycryptographicapplicationsbecauseitsinformationentropyissufficientforsecurity.Biometricauthenticationoffersnaturalandpasswordlessuserauthenticationinvarioussystemswherefuzzyextractorscanbeusedforbiometricinformationsecurity.Typically,abiometricsystemoperatesintwomodes:verificationandidentification.However,existingfuzzyextractorsdoesnotsupportefficientuseridentification.Inthispaper,weproposeasuccinctfuzzyextractorschemewhichenablesefficientbiometricidentificationaswellasverificationanditsatisfiessecurityrequirements.Weshowthattheproposedschemecanbeeasilyusedinbothverificationandidentificationmodes.Tothebestofourknowledge,weproposethefirstfuzzyextractorbasedbiometricidentificationprotocol.Theproposedprotocolisabletoidentifyauserwithconstantcomputationalcostratherthanlinear-timecomputationbyusingotherfuzzyextractorschemes.Biometricinformationsecurityisanimportantconcernofusingbiometricsystems,sothatwealsoprovidesecurityanalysisofproposedschemestoshowtheirsecurityboundaries.Theimplementationshowsthattheperformanceofproposedidentificationprotocolisconstantandclosetothatofverificationprotocols.

SmartphonePrivacyLeakageofSocialRelationshipsandDemographicsfromSurroundingAccessPoints ChenWang(StevensInstituteofTechnology),ChuyuWang(StevensInstituteofTechnology),YingyingChen(StevensInstituteofTechnology),LeiXie(NanjingUniversity),SangluLu(NanjingUniversity)

WhilethemobileusersenjoytheanytimeanywhereInternetaccessbyconnectingtheirmobiledevicesthroughWi-Fiservices,theincreasingdeploymentofaccesspoints(APs)haveraisedanumberofprivacyconcerns.ThispaperexploresthepotentialofsmartphoneprivacyleakagecausedbysurroundingAPs.Inparticular,westudytowhatextenttheuserspersonalinformationsuchassocialrelationshipsanddemographicscouldberevealedleveragingsimplesignalinformationfromAPswithoutexaminingtheWi-Fitraffic.OurapproachutilizesusersactivitiesatdailyvisitedplacesderivedfromthesurroundingAPstoinferuserssocialinteractionsandindividualbehaviors.Furthermore,wedeveloptwonewmechanisms:theCloseness-basedSocialRelationshipsInferencealgorithmcaptureshowcloselypeopleinteractwitheachotherbyevaluatingtheirphysicalclosenessandderivesfine-grainedsocialrelationships,whereastheBehavior-basedDemographicsInferencemethoddifferentiatesvariousindividualbehaviorsviatheextractedactivityfeatures(e.g.,activenessandtimeslots)ateachdailyplacetorevealusersdemographics.Extensiveexperimentsconductedwith21participantsrealdailylifeincluding257differentplacesinthreecitiesovera6-monthperioddemonstratethatthesimplesignalinformationfromsurroundingAPshaveahighpotentialtorevealpeoplessocialrelationshipsandinferdemographicswithanover90%accuracywhenusingourapproach.

EV-Matching:BridgingLargeVisualDataandElectronicDataforEfficientSurveillanceGangLi(TheOhioStateUniversity),FanYang(TheOhioStateUniversity),GuoxingChen(TheOhioStateUniversity),QiangZhai(TheOhioStateUniversity),XinfengLi(TheOhioStateUniversity),JinTeng(TheOhioStateUniversity),JundaZhu(UniversityofMacau),DongXuan(TheOhioStateUniversity),BiaoChen(UniversityofMacau),WeiZhao(UniversityofMacau)

Visual(V)surveillancesystemsareextensivelydeployedandbecomingthelargestsourceofbigdata.Ontheotherhand,electronic(E)dataalsoplaysanimportantroleinsurveillanceanditsamountincreasesexplosivelywiththeubiquityofmobiledevices.Oneofthemajorproblemsinsurveillanceistodeterminehumanobjectsidentitiesamongdifferentsurveillancescenes.TraditionalwayofprocessingbigVandEdatasetsseparatelydoesnotservethepurposewellbecauseVdataandEdataareimperfectaloneforinformationgatheringandretrieval.Matchinghumanobjectsinthetwodatasetscanmergethegoodofthetwoforefficientlarge-scalesurveillance.Yetsuchmatchingacrosstwoheterogeneousbigdatasetsischallenging.Inthispaper,weproposeanefficientsetofparallelalgorithms,calledEV-Matching,tobridgebigEandVdata.WematchEandVdatabasedontheirspatiotemporalcorrelation.TheEV-MatchingalgorithmsareimplementedonApacheSparktofurtheracceleratethewholeprocedure.Weconductextensiveexperimentsonalargesyntheticdatasetunderdifferentsettings.Resultsdemonstratesthefeasibilityandefficiencyofourproposedalgorithms.

AdaptiveReconnaissanceAttackswithNear-OptimalParallelBatching XiangLi(UniversityofFlorida),JohnathanSmith(UniversityofFlorida),MyThai(UniversityofFlorida)

Inassessingprivacyononlinesocialnetworks,itisimportanttoinvestigatetheirvulnerabilitytoreconnaissancestrategies,inwhichattackersluretargetsintobeingtheirfriendsbyexploitingthesocialgraphinordertoextractvictimssensitiveinformation.Asthenetworktopologyisonlypartiallyrevealedaftereachsuccessfulfriendrequest,attackersneedtoemployanadaptivestrategy.Existingworkonlyconsideredasimplestrategyinwhichattackerssequentiallyacquireonefriendatatime,whichcausestremendousdelayinwaitingforresponsesbeforesendingthenextrequest,andwhichlacktheabilitytoretryfailedrequestsafterthenetworkhaschanged.Incontrast,weinvestigateanadaptiveandparallelstrategy,ofwhichattackerscansimultaneouslysendmultiplefriendrequestsinbatchandrecoverfromfailedrequestsbyretryingaftertopologychanges,therebysignificantlyreducingthetimeofreachingthetargetsandimprovingrobustness.Wecastthisapproachasanoptimizationproblem,Max-Crawling,andshowitinapproximablewithin(1_1/e+).WefirstdesignourcorealgorithmPM-AReSTwhichhasanapproximationratioof(1_e(1/e_1))usingadaptivemonotonicsubmodularproperties.Wenextprovideanear-optimalsolution((1_1/e))viaatwostagestochasticprogrammingapproach.Wefurtherestablishthegapboundof(1_e_(1_1/e)2)betweenbatchstrategiesversustheoptimalsequentialone.Weexperimentallyvalidateourtheoreticalresults,findingthatouralgorithmperformsnearoptimallyinpracticeandthatthisisrobustunderavarietyofproblemsettings.

AchievingStrongPrivacyinOnlineSurvey YouZhou(UniversityofFlorida),YianZhou(UniversityofFlorida),ShigangChen(UniversityofFlorida),SamuelS.Wu(UniversityofFlorida)

ThankstotheproliferationofInternetaccessandmoderndigitalandmobiledevices,onlinesurveyhasbeenflourishingintodatacollectionofmarketing,social,financialandmedicalstudies.However,traditionaldatacollectionmethodsinonlinesurveysufferfromseriousprivacyissues.Existingprivacyprotectiontechniquesarenotadequateforonlinesurveyforlackofstrongprivacy.Inthispaper,weproposeapracticalstrongprivacyonlinesurveyschemeSPSbasedonanoveldatacollectiontechniquecalleddualmatrixmasking(DM2),whichguaranteesthecorrectnessofthetallyingresultswithlowcomputationoverhead,andachievesuniversalverifiability,robustnessandstrongprivacy.WealsoproposeamorerobustschemeRSPS,whichincorporatesmultipledistributedsurveymanagers.The

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RSPSschemepreservesthenicepropertiesofSPS,andfurtherachievesrobuststrongprivacyagainstjointcollusionattack.Throughextensiveanalyses,wedemonstrateourproposedschemescanbeefficientlyappliedtoonlinesurveywithaccuracyandstrongprivacy.

Research12:CloudComputingandDistributedDataAnalyticsServiceOverlayForestEmbeddingforSoftware-DefinedCloudNetworks Jian-JhihKuo(AcademiaSinica),Shan-HsiangShen(NationalTaiwanUniversityofScienceandTechnology),Ming-HongYang(UniversityofMinnesota),De-NianYang(AcademiaSinica),Ming-JerTsai(NationalTsingHuaUniversity),Wen-TsuenChen(AcademiaSinica)

NetworkFunctionVirtualization(NFV)onSoftwareDefinedNetworks(SDN)caneffectivelyoptimizetheallocationofVirtualNetworkFunctions(VNFs)andtheroutingofnetworkflowssimultaneously.Nevertheless,mostpreviousstudiesonNFVfocusonunicastservicechainsandtherebyarenotscalabletosupportalargenumberofdestinationsinmulticast.Ontheotherhand,theallocationofVNFshasnotbeensupportedinthecurrentSDNmulticastroutingalgorithms.Inthispaper,therefore,wemakethefirstattempttotackleanewchallengingproblemforfindingaserviceforestwithmultipleservicetrees,whereeachtreecontainsmultipleVNFsrequiredbyeachdestination.Specifically,weformulateanewoptimization,namedServiceOverlayForest(SOF),tominimizethetotalcostofallallocatedVNFsandallmulticasttreesintheforest.Wedesignanew3_ST-approximationalgorithmtosolvetheproblem,where_STdenotesthebestapproximationratiooftheSteinerTreeproblem,andthedistributedimplementationofthealgorithmisalsopresented.Simulationresultsonrealnetworksfordatacentersmanifestthattheproposedalgorithmoutperformstheexistingonesbyover25%.Moreover,theimplementationofanexperimentalSDNwithHPOpenFlowswitchesindicatesthatSOFcansignificantlyimprovetheQoEoftheYoutubeservice.

JointOptimizationofChainPlacementandRequestSchedulingforNetworkFunctionVirtualization QixiaZhang(HuazhongUniversityofScience&Technology),YikaiXiao(HuazhongUniversityofScience&Technology),FangmingLiu(HuazhongUniversityofScienceandTechnology),JohnChiShingLui(ChineseUniversityofHongKong),JianGuo(HuazhongUniversityofScience&Technology),TaoWang(HuazhongUniversityofScience&Technology)

ComparedwithexecutingNetworkFunctions(NFs)ondedicatedhardwares,therecenttrendofNetworkFunctionVirtualization(NFV)holdsthepromiseforoperatorstoflexiblydeploysoftware-basedNFsoncommodityservers.However,virtualNFs(VNFs)arenormallychainedtogethertoprovideaspecificnetworkservice.Thus,anefficientschemeisneededtoplacetheVNFchainsacrossthenetworkandeffectivelyschedulerequeststoserviceinstances,whichcanmaximizetheaverageresourceutilizationofeachnodeinserviceandsimultaneouslyminimizetheaverageresponselatencyofeachrequest.Tothisend,weformulatefirstVNFchainsplacementproblemasavariantofbin-packingproblem,whichisNP-hard,andwemodelrequestschedulingproblembasedonthekeyconceptsfromopenJacksonnetwork.TojointlyoptimizetheperformanceofNFV,weproposeapriority-drivenweightedalgorithmtoimproveresourceutilizationandaheuristicalgorithmtoreduceresponselatency.Throughextensivetrace-drivensimulations,weshowthatourmethodscanindeedenhanceperformanceindiversescenarios.Inparticular,wecanimprovetheaverageresourceutilizationby24.9%andcanreducetheaveragetotallatencyby19.9%ascomparedwithotherstate-of-the-artmethods.

BIGCacheAbstractionforCacheNetworks EmanRamadan(UniversityofMinnesota),ArvindNarayanan(UniversityofMinnesota),Zhi-LiZhang(UniversityofMinnesota),RunhuiLi(HuaweiFutureNetworkTheoryLab),GongZhang(HuaweiFutureNetworkTheoryLab)

Inthispaper,weadvocatethenotionofBIGcacheasaninnovativeabstractionforeffectivelyutilizingthedistributedstorageandprocessingcapacitiesofallserversinacachenetwork.TheBIGcacheabstractionisproposedtopartlyaddresstheproblemof(cascade)thrashinginahierarchicalnetworkofcacheservers,whereithasbeenknownthatcacheresourcesatintermediateserversarepoorlyutilized,especiallyunderclassicalcachereplacementpoliciessuchasLRU.WelayouttheadvantagesofBIGcacheabstractionandmakeastrongcasebothfromatheoreticalstandpointaswellasthroughsimulationanalysis.WealsodevelopthedCLIMBcachealgorithmtominimizetheoverheadsofmovingobjectsacrossdistributedcacheboundariesandpresentasimpleyeteffectiveheuristicforaddressingthecacheallotmentprobleminthedesignofBIGcacheabstraction.

DistributedQRdecompositionframeworkfortrainingSupportVectorMachines JyotikrishnaDass(TexasA&MUniversity),V.N.S.PrithviSakuru(TexasA&MUniversity),VivekSarin(TexasA&MUniversity),RabiN.Mahapatra(TexasA&MUniversity)

SupportVectorMachines(SVM)belongtoclassofsupervisedmachinelearningalgorithmswithapplicationsinclassificationandregressionanalysis.SVMtrainingismodeledasaconvexoptimizationproblemthatiscomputationallytediousandhaslargememoryrequirements.Specifically,itisaquadraticprogrammingproblemwhichscalesrapidlywiththetrainingsetsizeratherthanthedimensionalityofthefeaturespace.Inthiswork,wefirstpresentanovelQRdecompositionframework(QRSVM)toefficientlymodelandsolvealargescaleSVMproblembycapitalizingonlow-rankrepresentationsofthefullkernelmatrixratherthansolvingtheproblemasasequenceofsmallersub-problems.Thelow-rankstructureofthekernelmatrixisleveragedtotransformthedensematrixintoonewithasparseandseparablestructure.ThemodifiedSVMproblemrequiressignificantlylessermemoryandcomputation.Ourapproachscaleslinearlywiththetrainingsetsizewhichmakesitapplicabletolargedatasets.Thismotivatestowardsouranothercontribution;exploringadistributedQRSVMframeworktosolvelarge-scaleSVMclassificationproblemsinparallelacrossaclusterofcomputingnodes.Wealsoderiveanoptimalstepsizeforfastconvergenceofthedualascentmethodwhichisusedtosolvethequadraticprogrammingproblem.

DistributivelyComputingRandomWalkBetweennessCentralityinLinearTime Qiang-ShengHua(HuazhongUniversityofScienceandTechnology),MingAi(HuazhongUniversityofScienceandTechnology),HaiJin(HuazhongUniversityofScienceandTechnology),DongxiaoYu(HuazhongUniversityofScienceandTechnology),XuanhuaShi(HuazhongUniversityofScienceandTechnology)

Betweennesscentralityofanoderepresentsitsinfluenceoverthespreadofinformationinthenetwork.Itisnormallydefinedastheratioofthenumberofshortestpathspassingthroughthenodeamongallshortestpaths.However,thespreadofinformationmaynotjustpassthroughtheshortestpathswhichiscapturedbyanewmeasureofbetweennesscentralitybasedonrandomwalks[1].Therandomwalkbetweennesscentralityofanodemeanshowoftenitistraversedbyarandomwalkbetweenallpairsofothernodes.Inthispaper,weproposeanO(nlogn)timedistributedrandomizedapproximationalgorithmforcalculatingeachnodesrandomwalkbetweennesscentralitywithanapproximationratio(1_)wherenisthenumberofnodesandisanarbitrarilysmallconstantbetween0and1.OurdistributedalgorithmisdesignedunderthewidelyusedCONGESTmodel,whereeachedgecanonlytransferO(logn)bitsineachround.Toourbest

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knowledge,thisisthefirstdistributedalgorithmforcomputingtherandomwalkbetweennesscentrality.Moreover,wegiveanon-triviallowerboundfordistributivelycomputingtheexactrandomwalkbetweennesscentralityundertheCONGESTmodel,whichis(nlogn+D)whereDisthenetworkdiameter.Thismeansexactlycomputingrandomwalkbetweennesscannotbedoneinsublineartime.

DeGPar:LargeScaleTopicDetectionusingNode-CutPartitioningonDenseWeightedGraphs KambizGhoorchian(RoyalInstituteofTechnology(KTH)),SarunasGirdzijauskas(RoyalInstituteofTechnology(KTH)),FatemehRahimian(SwedishInstituteofComputerScience(SICS))

TopicDetection(TD)referstoautomatictechniquesforlocatingtopicallyrelatedmaterialinwebdocuments[1].Nowadays,massiveamountsofdocumentsaregeneratedbyusersofOnlineSocialNetworks(OSNs),informofveryshorttext,tweetsandsnippetsofnews.Whiletopicdetection,initstraditionalform,isappliedtoafewdocumentscontainingalotofinformation,theproblemhasnowchangedtodealingwithmassivenumberofdocumentswithverylittleinformation.Thetraditionalsolutions,thus,fallshorteitherinscalability(duetohugenumberofinputitems)orsparsity(duetoinsufficientinformationperinputitem).InthispaperweaddressthescalabilityproblembyintroducinganefficientandscalablegraphbasedalgorithmforTDonshorttexts,leveragingdimensionalityreductionandclusteringtechniques.Wefirst,compresstheinputsetofdocumentsintoadensegraph,suchthatfrequentcooccurrencepatternsinthedocumentscreatemultipledensetopologicalareasinthegraph.Then,wepartitionthegraphintomultipledensesub-graphs,eachrepresentingatopic.Wecomparetheaccuracyandscalabilityofoursolutionwithtwostate-of-the-artsolutions(includingthestandardLDA,andBiTerm).Theresultsontwowidelyusedbenchmarkdatasetsshowthatouralgorithmnotonlymaintainsasimilarorbetteraccuracy,butalsoperformsbyanorderofmagnitudefasterthanthestate-of-the-artapproaches.

Research13:DistributedAlgorithmsandTheoryIINetworkedStochasticMulti-ArmedBanditswithCombinatorialStrategies ShaojieTang(UniversityofTexasatDallas),YaqinZhou(SUTD),KaiHan(UniversityofScienceandTechnologyofChina),ZhaoZhang(ZhejiangNormalUniversity),JingYuan(UniversityofTexasatDallas),WeiliWu(UniversityofTexasatDallas)

Inthispaper,weinvestigatealargelyextendedversionofclassicalMABproblem,callednetworkedcombinatorialbanditproblems.Inparticular,weconsiderthesettingofadecisionmakeroveranetworkedbanditsasfollows:eachtimeacombinatorialstrategy,e.g.,agroupofarms,ischosen,andthedecisionmakerreceivesarewardresultingfromherstrategyandalsoreceivesasidebonusresultingfromthatstrategyforeacharmsneighbor.Thisismotivatedbymanyrealapplicationssuchason-linesocialnetworkswherefriendscanprovidetheirfeedbackonsharedcontent,thereforeifwepromoteaproducttoauser,wecanalsocollectfeedbackfromherfriendsonthatproduct.Tothisend,weconsidertwotypesofsidebonusinthisstudy:sideobservationandsidereward.Uponthenumberofarmspulledateachtimeslot,westudytwocases:single-playandcombinatorial-play.Consequently,thisleavesusfourscenariostoinvestigateinthepresenceofsidebonus:Single-playwithSideObservation,Combinatorial-playwithSideObservation,Single-playwithSideReward,andCombinatorial-playwithSideReward.Foreachcase,wepresentandanalyzeaseriesofzeroregretpoliceswheretheexpectofregretovertimeapproacheszeroastimegoestoinfinity.Extensivesimulationsvalidatetheeffectivenessofourresults.

ComputabilityofPerpetualExplorationinHighlyDynamicRings MarjorieBournat(UPMCSorbonneUniversités),SwanDubois(UPMCSorbonneUniversités),FranckPetit(UPMCSorbonneUniversités)

Weconsidersystemsmadeofautonomousmobilerobotsevolvinginhighlydynamicdiscreteenvironmenti.e.,graphswhereedgesmayappearanddisappearunpredictablywithoutanyrecurrence,stability,norperiodicityassumption.Robotsareuniform(theyexecutethesamealgorithm),theyareanonymous(theyaredevoidofanyobservableID),theyhavenomeansallowingthemtocommunicatetogether,theysharenocommonsenseofdirection,andtheyhavenoglobalknowledgerelatedtothesizeoftheenvironment.However,eachofthemisendowedwithpersistentmemoryandisabletodetectwhetheritstandsaloneatitscurrentlocation.Ahighlydynamicenvironmentismodeledbyagraphsuchthatitstopologykeepscontinuouslychangingovertime.Inthispaper,weconsideronlydynamicgraphsinwhichnodesareanonymous,eachofthemisinfinitelyoftenreachablefromanyotherone,andsuchthatitsunderlyinggraph(i.e.,thestaticgraphmadeofthesamesetofnodesandthatincludesalledgesthatarepresentatleastonceovertime)formsaringofarbitrarysize.Inthiscontext,weconsiderthefundamentalproblemofperpetualexploration:eachnodeisrequiredtobeinfinitelyoftenvisitedbyarobot.Thispaperanalyzesthecomputabilityofthisproblemin(fully)synchronoussettings,i.e.,westudythedeterministicsolvabilityoftheproblemwithrespecttothenumberofrobots.Weprovidethreealgorithmsandtwoimpossibilityresultsthatcharacterize,foranyringsize,thenecessaryandsufficientnumberofrobotstoperformperpetualexplorationofhighlydynamicrings.

LocallySelf-AdjustingSkipGraphs SikderHuq(TheUniversityofIowa),SukumarGhosh(TheUniversityofIowa)

Wepresentadistributedself-adjustingalgorithmforskipgraphsthatminimizestheaverageroutingcostsbetweenarbitrarycommunicationpairsbyperformingtopologicaladaptationtothecommunicationpattern.Ouralgorithmisfullydecentralized,conformstotheCONGESTmodel(i.e.O(logn)bitmessages),andrequiresO(logn)bitsofmemoryforeachnode,wherenisthetotalnumberofnodes.Uponeachcommunicationrequest,ouralgorithmfirstestablishescommunicationbyusingthestandardskipgraphrouting,andthenlocallyandpartiallyreconstructstheskipgraphtopologytoperformtopologicaladaptation.Weproposeacomputationalmodelforsuchalgorithms,aswellasayardstick(workingsetproperty)toevaluatethem.Ourworkingsetpropertycanalsobeusedtoevaluateself-adjustingalgorithmsforothergraphclasseswheremultipletree-likesubgraphsoverlap(e.g.hypercubenetworks).Wederivealowerboundoftheamortizedroutingcostforanyalgorithmthatfollowsourmodelandservesanunknownsequenceofcommunicationrequests.Weshowthattheroutingcostofouralgorithmisatmostaconstantfactormorethantheamortizedroutingcostofanyalgorithmconformingtoourcomputationalmodel.Wealsoshowthattheexpectedtransformationcostforouralgorithmisatmostalogarithmicfactormorethantheamortizedroutingcostofanyalgorithmconformingtoourcomputationalmodel.

OnlinetoOfflineBusiness:UrbanTaxiDispatchingwithPassenger-DriverMatchingStability HuanyangZheng(TempleUniversity),JieWu(TempleUniversity)

IntheOnlinetoOffline(O2O)taxibusiness(e.g.,Uber),theinterestsofpassengers,taxidrivers,andthecompanymaynotalignwitheachother,sincetaxisdonotbelongtothecompany.Tobalancethoseinterests,thispaperstudiesthetaxidispatchproblemfortheO2Otaxibusiness.Theinterestsofpassengersandtaxidriversaremodeled.Fornon-sharingtaxidispatches(multiplepassengerrequestscannotshareataxi),astablemarriageapproachisproposed.Itcandealwithunequalnumbersofpassengerrequestsandtaxisthroughmatchingthemtodummypartners.Theexistenceofstablematchingswithdummypartnersisproved.

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Threerulesarepresentedtofindoutallpossiblestablematchings.Forsharingtaxidispatches(multiplepassengerrequestscouldshareataxi),passengerrequestsarepackedthroughsolvingamaximumsetpackingproblem.Packedpassengerrequestsareregardedasasinglerequestformatchingtaxis.Extensiverealdata-drivenexperimentsdemonstratetheperformanceofourapproach.Theproposedalgorithmshavealimitedperformancegaptotheliteratureintermsofthedispatchdelayandthepassengersatisfactory,butsignificantlyimprovestheexistingalgorithmsintermsofthetaxisatisfactory.IndexTermsTaxidispatchschedule,passengerrequests,taxidrivers,matchingstability,sharingandnon-sharing.

AnOptimizationFrameworkForOnlineRide-sharingMarkets YongzhengJia(TsinghuaUniversity),WeiXu(TsinghuaUniversity),XueLiu(McGillUniversity)

Taxiservicesandproductdeliveryservicesareinstrumentalforourmodernsociety.Thankstotheemergenceofsharingeconomy,ride-sharingservicessuchasUber,Didi,LyftandGooglesWazeRiderarebecomingmoreubiquitousandgrowintoanintegralpartofoureverydaylives.However,theefficiencyoftheseservicesareseverelylimitedbythesub-optimalandimbalancedmatchingbetweenthesupplyanddemand.Weneedageneralizedframeworkandcorrespondingefficientalgorithmstoaddresstheefficientmatching,andhenceoptimizetheperformanceofthesemarkets.Existingstudiesfortaxianddeliveryservicesareonlyapplicableinscenariosoftheone-sidedmarket.Incontrast,thisworkinvestigatesahighlygeneralizedmodelforthetaxianddeliveryservicesinthemarketeconomy(abbreviatedastaxianddeliverymarket)thatcanbewidelyusedintwo-sidedmarkets.Further,wepresentefficientonlineandofflinealgorithmsfordifferentapplications.Weverifyouralgorithmwiththeoreticalanalysisandtrace-drivensimulationsunderrealisticsettings.

FastandAccurateTrackingofPopulationDynamicsinRFIDSystems MuhammadShahzad(NorthCarolinaStateUniversity),AlexLiu(MichiganStateUniversity)

RFIDsystemshavebeenwidelydeployedforvariousapplicationssuchassupplychainmanagement,indoorlocalization,inventorycontrol,andaccesscontrol.ThispaperdealswiththefundamentalproblemofestimatingthenumberofarrivinganddepartingtagsbetweenanytwotimeinstantsindynamicallychangingRFIDtagpopulations,whichisneededinmanyapplicationssuchaswarehousemonitoringandprivacysensitiveRFIDsystems.Inthispaper,weproposeadynamictagestimationscheme,namelyDTE,thatcanachievearbitrarilyhighrequiredreliability,iscompliantwiththeC1G2standard,andworksinsingleaswellasmultiple-readerenvironment.DTEusesthestandardizedframeslottedAlohaprotocolandutilizesthenumberofslotsthatchangetheirvaluesincorrespondingAlohaframesatthetwotimeinstantstoestimatethenumberofarrivinganddepartingtags.Itiseasytodeploybecauseitneitherrequiresmodificationtotagsnortothecommunicationprotocolbetweentagsandreaders.WehaveextensivelyevaluatedandcomparedDTEwiththeonlypriorscheme,ZDE,thatcanestimatethenumberofarrivinganddepartingtags.Unfortunately,ZDEcannotachievearbitrarilyhighrequiredreliability.Incontrast,ourproposedschemealwaysachievestherequiredreliability.Forexample,foratagpopulationcontaining104tags,arequiredreliabilityof95%,andarequiredconfidenceintervalof5%,DTEtakes5.12secondstoachievetherequiredreliabilitywhereasZDEachievesareliabilityofonly66%inthesameamountoftime.

Research14:MobileandWirelessComputingSystemsIIRobustIndoorWirelessLocalizationUsingSparseRecovery WeiGong(SimonFraserUniversity),JiangchuanLiu(SimonFraserUniversity)

Withthemulti-antennadesignofWiFiinterfaces,phasedarrayhasbecomeapromisingmechanismforaccurateWiFilocalization.State-of-the-artWiFi-basedsolutionsusingAoA(Angle-of-Arrival),however,faceanumberofcriticalchallenges.First,theirlocalizationaccuracydegradesdramaticallywhentheSignal-to-NoiseRatio(SNR)becomeslow.Second,theydonotfullyutilizecoherentprocessingacrossallavailabledomains.Inthispaper,wepresentROArray,aRObustArraybasedsystemthataccuratelylocalizesatargetevenwithlowSNRs.First,inthespatialdomain,ROArraycanproducesharpAoAspectrumsbyparameterizingthesteeringvectorbasedonasparsegrid.Then,toexpandintothefrequencydomain,itjointlyestimatestheToAs(Time-of-Arrival)andAoAsofallthepathsusingmulti-subcarrierOFDMmeasurements.Furthermore,throughmulti-packetfusion,ROArrayisenabledtoperformcoherentestimationacrossthespatial,frequency,andtimedomains.Suchcoherentprocessingnotonlyincreasesthevirtualaperturesize,whichenlargesthenumberofmaximumresolvablepaths,butalsoimprovesthesystemrobustnesstonoises.Ourimplementationusingoff-the-shelfWiFicardsdemonstratesthat,withlowSNRs,ROArraysignificantlyoutperformsstate-ofthe-artsolutionsinlocalizationaccuracy;whenmediumorhighSNRsarepresent,itachievescomparableaccuracy.

Max-MinFairResourceAllocationinHetNets:DistributedAlgorithmsandHybridArchitecture EhsanAryafar(PortlandStateUniversity),AlirezaKeshavarz-Haddad(ShirazUniversity),CarleeJoe-Wong(CarnegieMellonUniversity),MungChiang(PrincetonUniversity)

WestudytheresourceallocationprobleminRANlevelintegratedHetNets.ThisemergingHetNetsparadigmallowsfordynamictrafficsplittingacrossradioaccesstechnologiesforeachclient,andthenforaggregatingthetrafficinsidethenetworktoimprovetheoverallresourceutilization.Wefocusonthemaxminfairservicerateallocationacrosstheclients,andstudythepropertiesoftheoptimalsolution.Basedontheanalysis,wedesignalowcomplexitydistributedalgorithmthattriestoachievemax-minfairness.Wealsodesignahybridnetworkarchitecturethatleveragesopportunisticcentralizednetworksupervisiontoaugmentthedistributedsolution.Weanalyzetheperformanceofourproposedalgorithmsandprovetheirconvergence.Wealsoderiveconditionsunderwhichtheoutcomeisoptimal.Whentheconditionsarenotsatisfied,weprovideconstantupperandlowerboundsontheoptimalitygap.Finally,westudytheconvergencetimeofourdistributedsolutionandshowthatleveragingappropriatepoliciesinitsdesignsignificantlyreducestheconvergencetime.

OptimizationofFull-ViewBarrierCoveragewithRotatableCameraSensors XiaofengGao(ShanghaiJiaoTongUniversity),RuiYang(UniversityofIllinoisUrbana-Champaign),FanWu(ShanghaiJiaoTongUniversity),GuihaiChen(ShanghaiJiaoTongUniversity),JingguangZhou(ShanghaiJiaoTongUniversity)

Inalltheresearchesinwirelesssensornetworks,camerasareincreasinglyutilizedfortheirsurveillancecapabilities.Inthispaper,weelaboratelydiscussabouttheproblemofFull-ViewBarrierCoveragewithRotatableCameraSensors(FBR),includingweaklyandstronglyconnectedversions.FBRisproventobeNP-hardinthispaperbyreducingGroupSteinerTreeproblemtoit.Ourgoalistoreducesensornumberwhenguaranteeingthesurveillancecapabilitiesatthesametime.Correspondingly,weintroduceanovelweighedgraphstructurecalledFull-ViewBarrierGraph.Wetransformweakversionproblemintoapseudoone-dimensiononeandproposeWGraProjalgorithmwiththehelpofdynamicprogramming;instrongversionproblem,weintroducetwocentralizedalgorithms(S-Dijkstra,S-Thorup),respectivelyaimingtosavesensornumberandtoreducetimecomplexity.Moreover,werigorouslyanalyzethecorrectnessandtimecomplexityforeachalgorithm.Inaddition,themassnumberofexperimentsareconductedtovalidatetheefficiencyofallalgorithms,whichprovethatourstructuresandalgorithmscanconstructafull-viewbarrierwithfewercamerasensorscomparedwithpreviousresearches.

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CommunicationthroughSymbolSilence:TowardsFreeControlMessagesinIndoorWLANs BingFeng(UniversityofScienceandTechnologyofChina),JianqingLiu(UniversityofFlorida),ChiZhang(UniversityofScienceandTechnologyofChina),YuguangFang(UniversityofFlorida)

Efficientdesignofwirelessnetworksbenefitsfromtheexchangeofcontrolmessages.However,controlmessageitselfconsumesscarcechannelresources.Inthispaper,weproposeCoS(CommunicationthroughsymbolSilence),anovelcommunicationstrategythatconveyscontrolmessagesforfreewithoutconsumingextrachannelresources.CoSinsertssilencesymbolsindatapacketsandleveragestheintervalsbetweeninsertedsilencesymbolstoencodeinformation.Thesilencesymbolscanbelocatedbyenergydetectionatthegranularityofsymbolsandtheintervalsareinterpretedintotransmittedcontrolmessages.Basedonourkeyinsightsthatthechannelcodeisunder-utilizedincurrentwirelessnetworksandthedistributionofsymbolerrorswithinadatapacketispredictableinindoorwirelesstransmissions,thesymbolserasedbysilencesymbolsarerecoveredbythecodingredundancythatisoriginallyusedtocorrectsymbolerrors.Arateadaptationschemeisdesignedtodynamicallyadjusttherateoffreecontrolmessagesaccordingtochannelconditionssothatthetransmissionoffreecontrolmessagesdoesnotharmtheoriginaldatathroughput.WeimplementCoSonoursoftwaredefinedradioplatformtovalidatethefeasibilityofCoS.Theextensiveresultsshowthatthecontrolmessagesaredeliveredwithcloseto100%accuracyinalargeSNRrange.Inaddition,wemeasuretheachievablecapacityoffreecontrolmessagesinvariouschannelconditions.

Secureconnectivityofwirelesssensornetworksunderkeypredistributionwithon/offchannels JunZhao(CarnegieMellonUniversity)

Theq-compositekeypredistributionscheme[1]isusedprevalentlyforsecurecommunicationsinlarge-scalewirelesssensornetworks(WSNs).Priorwork[2][4]exploressecureconnectivityinWSNsemployingtheq-compositeschemeforq=1withunreliablecommunicationlinksmodeledasindependenton/offchannels.Inthispaper,weinvestigatesecureconnectivityinWSNsoperatingundertheq-compositeschemeforgeneralqandundertheon/offchannelmodel.Wepresentconditionsonhowtoscalethemodelparameterssothatthenetworkissecurelyconnectedwithhighprobabilitywhenthenumberofsensorsbecomeslarge.Theresultsaregivenintheformofzero-onelaws.Numericalexperimentsconfirmthevalidityofouranalyticalresults.

iUpdater:LowCostRSSFingerprintsUpdatingforDevice-freeLocalization LiqiongChang(NorthwestUniversity),JieXiong(SingaporeManagementUniversity),YuWang(UniversityofNorthCarolinaatCharlotte),XiaojiangChen(NorthwestUniversity),JunhaoHu(NorthwestUniversity),FangDingyi(NorthwestUniversity)

Whilemostexistingindoorlocalizationtechniquesaredevice-based,manyemergingapplicationssuchasintruderdetectionandelderlycaredrivetheneedsofdevice-freelocalization,inwhichthetargetcanbelocalizedwithoutanydeviceattached.Amongthediversetechniques,receivedsignalstrength(RSS)fingerprint-basedmethodsarepopularbecauseofthewideavailabilityofRSSreadingsinmostcommodityhardware.However,currentfingerprint-basedsystemssufferfromhighhumanlaborcosttoupdatesofthefingerprintdatabaseandlowaccuracyduetolargedegreeofRSSvariations.Inthispaper,weproposeafingerprint-baseddevice-freelocalizationsystemnamediUpdatertosignificantlyreducethelaborcostandincreasetheaccuracy.Wepresentanovelselfaugmentedregularizedsingularvaluedecomposition(RSVD)methodintegratingthesparseattributewithuniquepropertiesofthefingerprintdatabase.iUpdaterisabletoaccuratelyupdatethewholedatabasewithRSSmeasurementsatasmallnumberofreferencelocations,thusreducingthehumanlaborcost.Furthermore,iUpdaterobservesthatalthoughtheRSSreadingsvariesalot,theRSSdifferencesbetweenboththeneighboringlocationsandadjacentwirelesslinksarerelativelystable.Thisuniqueobservationisappliedtoovercometheshort-termRSSvariationstoimprovethelocalizationaccuracy.Extensiveexperimentsinthreedifferentenvironmentsover3monthsdemonstratetheeffectivenessandrobustnessofiUpdater.

Research15:SocialNetworksandCrowdsourcingInfluenceMaximizationinaManyCascadesWorldIoulianaLitou(AUEB),VanaKalogeraki(AUEB),DimitriosGunopulos(UoA)

OnlineSocialNetworks(OSNs)arewidelyutilizedinviralmarketingcampaignsexploitingtheword-of-moutheffect.VariouspropagationmodelshavebeenproposedtodescribethewaycascadesunfoldinOSNs.Basedontheexistingpropagationmodels,severalstudiesaddresstheproblemofinfluencemaximization,wheretheobjectiveistoidentifyanappropriatesubsetofuserstoinitiatethespreadofacontagion.However,existingapproachesignoreanimportantfactorinthepropagationprocess,i.e.,thecorrelationofmultiplecontagionssimultaneouslycascadinginthesocialnetworkandhowtheseaffecttheusersdecisionsregardingtheadoptionofacontagion.Althoughrecentworkslookintoeitherthecompetitionorthecomplementarityamongapairofcontagions,auniformmodelthatdescribesthepropagationofmultiplecascadeswithvaryingtypesanddegreesofcorrelationsislacking.Thisworkconstitutesthefirstattempttofillthisgap.Weformulateanovelpropagationmodel,theCorrelatedContagionsDynamicLinearThreshold(CCDLT),thatconsidersthecorrelationofmanycontagionsineithercompetitiveorcomplementarymanner.Ourproposedmodelallowsfordifferentdegreesofcompetition/complementarityamongcascades.Wefurtherconsiderthatusersmaydynamicallyswitchstatesregardingthecontagiontheypromoteduringthepropagationprocess,basedontheinfluenceoftheirneighborhoods.Wethendesignagreedyseedselectionalgorithmthatidentifiestheappropriatesubsetofuserstoparticipateinaspecificcontagioninordertomaximizeitsspreadandweformallyprovethatitapproximatesthebestsolutionataratioof1_1/e.Throughanextensiveexperimentalevaluationwedemonstratethesuperiorityofourapproachoverexistingschemes.

Expertise-AwareTruthAnalysisandTaskAllocationinMobileCrowdsourcingXiaomeiZhang(UniversityofSouthCarolinaBeaufort),YiboWu(PennsylvaniaStateUniversity),LifuHuang(RensselaerPolytechnicInstitute),HengJi(RensselaerPolytechnicInstitute),GuohongCao(PennsylvaniaStateUniversity)

Mobilecrowdsourcinghasreceivedconsiderableattentionasitenablespeopletocollectandsharelargevolumeofdatathroughtheirmobiledevices.Sincetheaccuracyofthecollecteddataisusuallyhardtoensure,researchershaveproposedtechniquestoidentifytruthfromnoisydatabyinferringandutilizingthereliabilityofusers,andallocatetaskstouserswithhigherreliability.However,theyneglectthefactthatausermayonlyhaveexpertiseonsomeproblems(insomedomains),butnotothers.Neglectingthisexpertisediversitymaycausetwoproblems:lowestimationaccuracyintruthanalysisandineffectivetaskallocation.Toaddresstheseproblems,weproposeanExpertise-awareTruthAnalysisandTaskAllocation(ETA2)approach,whichcaneffectivelyinferuserexpertiseandthenallocatetasksandestimatetruthbasedontheinferredexpertise.ETA2reliesonanovelsemanticanalysismethodtoidentifytheexpertisedomainsofthetasksanduserexpertise,anexpertiseawaretruthanalysissolutiontoestimatetruthandlearnuserexpertise,andanexpertise-awaretaskallocationmethodtomaximizethe

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probabilitythattasksareallocatedtouserswiththerightexpertisewhileensuringtheworkloaddoesnotexceedtheprocessingcapabilityateachuser.Experimentalresultsbasedontworeal-worlddatasetsdemonstratethatETA2significantlyoutperformsexistingsolutions.

MeLoDy:ALong-termDynamicQuality-awareIncentiveMechanismforCrowdsourcing HongweiWang(ShanghaiJiaoTongUniversity),SongGuo(TheHongKongPolytechnicUniversity),JiannongCao(TheHongKongPolytechnicUniversity),MinyiGuo(ShanghaiJiaoTongUniversity)

CrowdsourcingallowsrequesterstoallocatetaskstoagroupofworkersontheInternettomakeuseoftheircollectiveintelligence.Qualitycontrolisakeydesignobjectiveinincentivemechanismsforcrowdsourcingasrequestersaimatobtaininganswersofhighqualityunderagivenbudget.However,whenmeasuringworkerslong-termquality,existingmechanismseitherfailtoutilizeworkershistoricalinformation,ortreatworkersqualityasstableandignoreitstemporalcharacteristics,henceperformingpoorlyinalongrun.InthispaperweproposeMELODY,along-termdynamicqualityawareincentivemechanismforcrowdsourcing.MELODYmodelsinteractionbetweenrequestersandworkersasreverseauctionsthatruncontinuously.IneachrunofMELODY,wedesignatruthful,individualrational,budgetfeasibleandquality-awarealgorithmfortaskallocationwithpolynomial-timecomputationcomplexityandO(1)performanceratio.Moreover,takingintoconsiderationthelong-termcharacteristicsofworkersquality,weproposeanovelframeworkinMELODYforqualityinferenceandparameterslearningbasedonLinearDynamicalSystemsattheendofeachrun,whichtakesfulladvantageofworkershistoricalinformationandpredictstheirqualityaccurately.Throughextensivesimulations,wedemonstratethatMELODYoutperformsexistingworkintermsofbothqualityestimation(reducingestimationerrorby17.6%_24.2%)andsocialperformance(increasingrequestersutilityby18.2%_46.6%)inlong-termscenarios.

TheStrongLinkGraphforEnhancingSybilDefenses SuhendryEffendy(NationalUniversityofSingapore),RolandYap(NationalUniversityofSingapore)

Thesybilproblemisafundamentalproblemindistributedsystemsandonlinesocialnetworks(OSNs).Thebasicproblemisthatanattackercaneasilycreatemultipleidentitiesinadistributedoropenonlinesystem.Popularandeffectivesybildefensesareusuallybasedonpropertiesofthenetworkstructure.However,mostdefensesassumethatitishardfortheattackertomakemanyconnectionstohonestusers.However,thisassumptioncanbeinvalidinrealOSNswhichdecreasestheeffectivenessofmanysybildefenses.Weproposeagraphtransformation,thestronglinkgraph,tomitigatesuchattacksbyreducingtheeffectofalargenumberofattackedges.Ourpreliminaryexperimentsshowindeedthatwhentheattackerhasmanyattackedges,existingalgorithmssuchasSybilLimit,SybilRankandGatekeeperareineffective.Afterthestronglinkgraphisapplied,itdeletesmanyoftheattackedges,restoringtheeffectivenessofthesybildefenses.

MechanismDesignforMobileCrowdsensingwithExecutionUncertainty ZhenzheZheng(ShanghaiJiaoTongUniversity),ZhaoxiongYang(ShanghaiJiaoTongUniversity),FanWu(ShanghaiJiaoTongUniversity),GuihaiChen(ShanghaiJiaoTongUniversity)

Mobilecrowdsensinghasemergedasapromisingparadigmfordatacollectionduetoincreasinglypervasiveandpowerfulmobiledevices.Therehavebeenextensiveresearchworksthatproposeincentivemechanismsforcrowdsensing,buttheyallmaketheassumptionthatthemobileuserwillpositivelycompletetheallocatedsensingtask.Inthispaper,weconsideranewscenarioofcrowdsensingwhereausermayfailtocompletethetask.Forexample,wesupposetheusercontinuouslycollectdatawithhisdeviceinthebackground,andhecompletesthesensingtaskonlyifhepassesthroughthelocationofinterest.Duetotheusersmobilitypattern,hemaysucceedorfailinthetask.Itisanimportantissuefortheincentivemechanismtoensurefaulttoleranceforeachsensingtask.Wedesignreverseauctionstomodeltheinteractionbetweentheplatformandmobileusers,inwhichusersprobabilityofsuccessandcosttoperformthetasksareprivateinformation,andweaimtoguaranteethetaskstobecompletedwithhighprobability,whileminimizingthesocialcost.WeprovethatminimizingthesocialcostisanNP-hardproblem,andpresentmechanismsthatachievetruthfulnessandguaranteedapproximationratio.Weperformextensivesimulationstovalidatethedesirablepropertiesofourmechanisms.

TowardsScalableandDynamicSocialSensingUsingADistributedComputingFramework DanielZhang(UniversityofNotreDame),ChaoZheng(UniversityofNotreDame),DongWang(UniversityofNotreDame),DougThain(UniversityofNotreDame),XinMu(UniversityofNotreDame),GregMadey(UniversityofNotreDame),ChaoHuang(UniversityofNotreDame)

WiththerapidgrowthofonlinesocialmediaandubiquitousInternetconnectivity,socialsensinghasemergedasanewcrowdsourcingapplicationparadigmofcollectingobservations(oftencalledclaims)aboutthephysicalenvironmentfromhumansordevicesontheirbehalf.Afundamentalprobleminsocialsensingapplicationsliesineffectivelyascertainingthecorrectnessofclaimsandthereliabilityofdatasourceswithoutknowingeitherofthemapriori,whichisreferredtoastruthdiscovery.Whilesignificantprogresshasbeenmadetosolvethetruthdiscoveryproblem,someimportantchallengeshavenotbeenwelladdressedyet.First,existingtruthdiscoverysolutionsdidnotfullysolvethedynamictruthdiscoveryproblemwherethegroundtruthofclaimschangesovertime.Second,manycurrentsolutionsarenotscalabletolarge-scalesocialsensingeventsbecauseofthecentralizednatureoftheirtruthdiscoveryalgorithms.Third,theheterogeneityandunpredictabilityofthesocialsensingdatatrafficposeadditionalchallengestotheresourceallocationandsystemresponsiveness.Inthispaper,wedevelopedaScalableStreamingTruthDiscovery(SSTD)solutiontoaddresstheabovechallenges.Inparticular,wefirstdevelopedadynamictruthdiscoveryschemebasedonHiddenMarkovModels(HMM)toeffectivelyinfertheevolvingtruthofreportedclaims.WefurtherdevelopedadistributedframeworktoimplementthedynamictruthdiscoveryschemeusingWorkQueueinHTCondorsystem.WealsointegratedtheSSTDschemewithanoptimalworkloadallocationmechanismtodynamicallyallocatetheresources(e.g.,cores,memories)tothetruthdiscoverytasksbasedontheircomputationrequirements.WeevaluatedSSTDthroughrealworldsocialsensingapplicationsusingTwitterdatafeeds.Theevaluationresultsonthreereal-worlddatatraces(i.e.,BostonBombing,ParisShootingandCollegeFootball)showthattheSSTDschemeisscalableandoutperformsthestate-of-thearttruthdiscoverymethodsintermsofbotheffectivenessandefficiency.

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Industry and Experimentation Track Paper Abstracts

Industry1:CloudDataCentersandPerformancePhoenix:Constraintawareschedulingforheterogeneousdatacenters PrashanthThinakaran(PennsylvaniaStateUniversity),JashwantRajGunasekaran(PennsylvaniaStateUniversity),BikashSharma(MicrosoftCorp),MahmutKandemir(PennsylvaniaStateUniversity),ChitaDas(PennsylvaniaStateUniversity)

Today’sdatacentersareincreasinglybecomingdiverseregardingbothhardwareandsoftwarearchitecturesinordertosupportamyriadofapplications.Theseapplicationsarealsoheterogeneousintermsofjobresponsetimesandresourcerequirements(eg.,NumberofCores,GPUs,NetworkSpeed)andtheyareexpressedastaskconstraints.Constraintsareusedforensuringtaskperformanceguarantees/QualityofService(QoS)byenablingtheapplicationtoexpressitsspecificresourcerequirements.Whileseveralschedulershaverecentlybeenproposedthataimtoimproveoverallapplicationandsystemperformance,fewoftheseschedulersconsiderresourceconstraintsacrosstaskswhilemakingtheschedulingdecisions.Furthermore,latency-criticalworkloadsandshort-livedjobsthattypicallyconstituteabout90%ofthetotaljobsinadatacenterhavestrictQoSrequirements,whichcanbemitigatedbyminimizingthetaillatencythrougheffectivescheduling.Inthispaper,weproposePhoenix,aconstraint-awarehybridschedulertoaddressboththeseproblems(constraintawarenessandensuringlowtaillatency)byminimizingthejobresponsetimesatconstrainedworkersandproactivelyreorderingthetasks.WeuseanovelConstraintResourceVector(CRV)basedscheduling,whichinturnfacilitatesreorderingofthejobsinaqueuetominimizetaillatency.WehaveusedthepubliclyavailableGoogletracestoanalyzetheirconstraintcharacteristicsandhaveembeddedtheseconstraintsinClouderaandYahooclustertracesforstudyingtheimpactoftracesonsystemperformance.ExperimentswithGoogle,ClouderaandYahooclustertracesacross15,000workernodeclustershowsthatPhoeniximprovesthe99thpercentilejobresponsetimesonanaverageby1.9xacrossallthreetraceswhencomparedagainstastate-of-the-arthybridscheduler.Further,incomparisontootherdistributedschedulerlikeHawk,itimprovesthe90thand99thpercentilejobresponsetimesby4.5xand5xrespectively.

DualScalingVMsandQueries:Cost-effectiveLatencyCurtailment JuanPérez(UniversityofMelbourne),RobertBirke(IBMResearchZurich),MathiasBjörkqvist(IBMResearchZurich),LydiaY.Chen(IBMResearchZurich)

WimpyvirtualinstancesequippedwithsmallnumbersofcoresandRAMarepopularpublicandprivatecloudofferingsbecauseoftheirlowcostforhostingapplications.Thechallengeishowtorunlatency-sensitiveapplicationsusingsuchinstances,whichtradeoffperformanceforcost.Inthisstudy,weanalyticallyandexperimentallyshowthatsimultaneouslyscalingresourcesatcoarsegranularityandworkloads,i.e.,submittingmultiplequeryclonestodifferentservers,atfinegranularitycanovercometheperformancedisadvantagesofwimpyVMinstancesandachievestringentlatencytargetsthatareevenlowerthantheaverageexecutiontimesofwimpyservers.Tosuchanend,wefirstderiveaclosed-formanalysisforthelatencyunderanygivenVMprovisioningandqueryreplicationlevel,consideringcloningpoliciesthatcan(not)terminateoutstandingcloneswith(without)anoverhead.Validatedontrace-drivensimulations,ouranalysisisabletoaccuratelypredictthelatencyandefficientlysearchfortheoptimalnumberofVMsandclones.Secondly,wedevelopadualelasticscaler,DuoScale,thatdynamicallyscalesVMsandclonesaccordingtotheworkloaddynamicssoastoachievethetargetlatencyinacost-effectivemanner.TheeffectivenessofDuoScaleliesontheobservationthattheapplicationperformanceonlyscalessub-linearlywithincreasingverticalorhorizontalresourceprovisioning,i.e.,resourcesperVMornumberofVMs.WeevaluateDuoScaleagainstVM-onlyscalingstrategiesviaextensivetrace-drivensimulationsaswellasexperimentalresultsonacloudtest-bed.OurresultsshowthatDuoScaleisabletoachievethestringenttargetlatencybyusingclonesonwimpyVMswithcostsavingsupto50%,comparedtoscalingbrawnyVMsthathavebetterperformanceatahigherunitcost.

Aframeworkforenablingsecurityservicescollaborationacrossmultipledomains DanielMigault(EricssonSecurityResearch),MarcosSimplicioJunior(EscolaPolitécnica),BrunoBarros(EscolaPolitécnica),MakanPourzandi(EricssonSecurityResearch),ThiagoAlmeida(EscolaPolitécnica),EwertonAndrade(EscolaPolitécnica),TerezaCarvalho(EscolaPolitécnica)

CollaborationamongSecurityServiceFunctions(SSF)isexpectedtobecomeasessentialtoSECaaS(SECurityasaService)systemsaselasticityistoIaaS(InfrastructureasaService).Thevirtualizationopensnewerainnetworksecurityasnewsecurityappliancescanbecreatedondemandinappropriateplacesinthenetwork.Atthesametime,theincreasingsizeanddiversityofattacksmakeitnecessarytocomeupwithnewapproachesformoreefficientandmoreresilientsecuritymechanisms.Inthispaper,weproposeanewframeworkleveragingSDN(SoftwareDefinedNetworking)andSFC(ServiceFunctionChaining)toenhancethecollaborationamongdifferentSSFstomitigatelargescaleattacks.WedescribeaframeworkthatallowsSSFsfromdifferentdomainstonegotiateanddynamicallycontroltheamountofresourcesallocatedforcollaboration,inwhatwecalla“best-effort”collaborationmode.ThisSSFcollaborationframeworkcreatesadistributedmitigationsystemforhandlinglargescaleattacksinadynamicandscalablemanner.Theefficiencyandfeasibilityofthisframeworkisexperimentallyassessed,showingthatourapproachincurslowoverhead,increasestheamountoftraffictreatedbySSFsandreducesthedroppedtrafficduetothelackofresourcesfromthesecuritymechanisms.

GroupClusteringUsingInter-GroupDissimilarities DebessayFesehayeKassa(VMware),LeninSingaravelu(Google),Chien-ChiaChen(VMware),XiaoboHuang(VMware),AmitabhaBanerjee(VMware),RuijinZhou(VMware),RajeshSomasundaran(VMware)

Varioussystemshavenaturalgroupings.Forinstanceinlargescaledistributedsystem,wecanhavegroupsofvirtualand/orphysicaldevices.Asystemcanalsohavegroupsoftimeseriesdatasetscollectedatdifferenttimeintervals.Suchgroupsareusuallycharacterizedbymultidimensionalmetrics(features)set.Clusteringsuchgroupsusingtheirmultidimensionaldatasetshasvariousapplicationssuchasidentifyingdifferentperformancelevelsforanomalydetectionandloadbalancing.Traditionalalgorithmsfocusonclusteringasingletimeseriesdatasetandnotonsuchgroupswithmultidimensionalmetricsdatasets.Inthispaperwepresentthedesign,implementationandanalysisoftwosetsofgroupclusteringalgorithms.Thefirstsetiscalledone-to-manyasitgeneratesclustersofgroupsbycomparingeachgroupagainstallothergroups.Thesecondsetofalgorithmsiscalledpairwiseasitgeneratestheclustersofgroupsusingpairwisegroupdissimilaritymatrix.Bothsetsofalgorithmsfirstgenerategroupdissimilarityweightsusingmetricrankingalgorithms.Weimplementedthegroupclusteringalgorithmsbyextendingawellknownmachinelearningpackageandusingafront-endvisualizer.WevalidatedtheclusteringalgorithmsusingrealworlddatasetsontheVMwarevSANproduct.Experimentalresultsshowthat7outofthe8proposedalgorithmscangenerateexpectedclustersinatleast4outofthe6detailedexperiments.The3out

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of8proposedalgorithmscangeneratetheexpectedclustersin5outofthe6experiments.Oneofthepairwisealgorithmscangeneratetheexpectedclustersinall6ofthe6experiments.

ComprehensiveMeasurementandAnalysisoftheUser-PerceivedI/OPerformanceinaProductionLeadership-ClassStorageSystemLipengWan(OakRidgeNationalLaboratory),MatthewWolf(OakRidgeNationalLaboratory),FeiyiWang(OakRidgeNationalLaboratory),JongYoulCho(OakRidgeNationalLaboratory),GeorgeOstruchov(OakRidgeNationalLaboratory),ScottKlasky(OakRidgeNationalLaboratory)

WiththeincreaseofthescaleandintensityoftheparallelI/Oworkloadsgeneratedbythosescientificapplicationsrunningonhighperformancecomputingfacilities,understandingtheI/Odynamics,especiallytherootcauseoftheI/OperformancevariabilityanddegradationinHPCenvironment,havebecomeextremelycriticaltotheHPCcommunity.Inthispaper,werunextensiveI/Omeasuringtestsonaproductionleadership-classstoragesystemtocapturetheperformancevariabilitiesoflarge-scaleparallelI/O.AnalyzingtheseresultsanditsstatisticcorrelationrevealedsomevaluableinsightsintothecharacteristicsofthestoragesystemandtherootcauseofI/Operformancevariability.Further,weleveragethesefindingsandproposeanI/OmiddlewaredesignrefactoringwhichcanimprovetheperformanceoftheparallelI/Obyoptimizingthedatastripingandplacement.Ourpreliminaryevaluationresultsdemonstratetheproposedapproachcanreducetheaverageper-processwritelatencybyatleast80%andthemaximumper-processwritelatencybyatleast20%.

Industry2:MobileComputingandInternetofThingsLocation:Charleston1

OntheLimitsofSubsamplingofLocationTraces MudhakarSrivatsa(IBMT.J.WatsonResearchCenter),RaghuGanti(IBMT.J.WatsonResearchCenter),PrasantMohapatra(UCDavis)

Locationdatacollectionatasocietalscaleisincreasinglybecomingcommon-examplesofthisarecallanddatadetailrecordsintelecommunicationcompanies,GPSsamplescollectedbycarcompanies,andGPSsamplesfrommobiledevicesinmappingcompanies(e.g.,Google,Microsoft).Suchlargescalemobilitydatasetshaveapplicationsinurbanplanning,networkplanning,surveillance,andreal-timetrafficestimations.Thispaperaddressestheproblemofsubsamplinglocationtraceswhilepreservingtheamountofinformationpresentinsuchdatasets.Wepresentanovelsubsamplingtechniquethatisbasedonahierarchicalgeographicalencodingmechanism(geohash),thatallowsforefficientspatialclustersampling.Weanalyzethissubsamplingtechniquethroughvariousinformationtheoreticmeasurestoquantifythetotal“amount”ofinformationinadatasetfromalocationtraceperspectiveandevaluatethesemetricsinthecontextoftwolargescalemobilitydatasetsfromtelecommunicationcompanies-oneisthatofcalldetailrecordsandthesecondisthatofdatadetailrecords.Weshowthatsubsamplingdatainboththesecasesbyasmuchas75%doesnotsignificantlyreducethetotalamountofinformation,i.e.thedatasetcanbeusedsimilartotheoriginalversion.ThispaveswayforthecreationofbetterspaceandCPUefficientmodelsthatcansupportvariousapplicationsreliantoncollectivelocationtraces.

SOM-TC:Self-organizingmapforHierarchicalTrajectoryClustering PranitaDewan(IBMTJWatsonResearchCenter),RaghuGanti(IBMTJWatsonResearchCenter),MudhakarSrivatsa(IBMTJWatsonResearchCenter)

Trajectoryclusteringtechniqueshelpdiscoverinterestinginsightsfrommovingobjectdata,includingcommonroutesforpeopleandvehicles,anomaloussub-trajectories,etc.Existingtrajectoryclusteringtechniquesfailtotakeintoaccounttheuncertaintypresentinlocationdata.Inthispaper,weinvestigatetheproblemofclusteringtrajectorydataandproposeanovelalgorithmforclusteringsimilarfullandsub-trajectoriestogetherwhilemodelinguncertaintyinthisdata.Wedescribethenecessarypre-processingtechniquesforclusteringtrajectorydata,namelytechniquestodiscretizerawlocationdatausingPossibleWorldsemanticstocapturetheinherentuncertaintyinlocationdata,andtosegmentfulltrajectoriesintomeaningfulsub-trajectories.Asabaseline,weextendthewellknownK-meansalgorithmtoclustertrajectorydata.Wethendescribeandevaluateanewtrajectoryclusteringalgorithm,SOM-TC(Self-OrganizingMapBasedTrajectoryClustering),thatisinspiredfromtheself-organizingmaptechniqueandisatleast4xfasterthanthebaselineK-meansandcurrentdensitybasedclusteringapproaches.

ProcessingEncryptedandCompressedTime-SeriesData MatúšHarvan(EnovosLuxembourgS.A.),SamuelKimoto(OpenSystems),ThomasLocher(ABBCorporateResearch),Yvonne-AnnePignolet(ABBCorporateResearch),JohannesSchneider(UniversityofLiechtenstein)

Numerousapplications,e.g.,intheindustrialsector,producelargeamountsoftime-seriesdata,whichmustbestoredandmadeavailablefordistributedprocessing.Whileoutsourcingdatastorageandprocessingtothird-partyserviceprovidersoffersmanybenefits,itraisesdataprivacyissues.Inlightofthisproblem,techniqueshavebeenproposedtoshareonlyencrypteddatawiththeremoteserviceprovider,yetthecapabilitytorunmeaningfulqueriesoverthedataispreserved.However,timeseriesdataistypicallycompressedattheservertosavespace,whichisnoteasilypossiblewhendealingwithencrypteddata.Moreover,datamustbecompressedinsuchawaythatqueriescanstillbeexecutedefficiently.Asafirststepinthisdirection,wepresentanapproachthatpreservesdataprivacy,enablescompressionattheserver,andsupportsqueryingofthestoreddata.Ourevaluationusingrealworldtime-seriesdatashowsthatourcompressionmechanismcanreducetherequiredspacedrastically.Moreover,themedianrunningtimeofallconsideredqueriesincreasesmarginally,implyingthatcompressioncanbeintroducedwithoutsacrificingperformanceofqueryexecution.

CalvinConstrained-AFrameworkforIoTApplicationsinHeterogeneousEnvironments AmardeepMehta(UmeåUniversity),RamiBaddour(UniversitádellaSvizzeraitaliana),FredrikSvensson(EricssonResearch),HaraldGustafsson(EricssonResearch),ErikElmroth(UmeåUniversity)

CalvinisanIoTframeworkforapplicationdevelopment,deploymentandexecutioninheterogeneousenvironments,thatincludesclouds,edgeresources,andembeddedorconstrainedresources.InsideCalvin,allthedistributedresourcesareviewedasoneenvironmentbytheapplication.Theframeworkprovidesmulti-tenancyandsimplifiesdevelopmentofIoTapplications,whicharerepresentedusingadataflowofapplicationcomponents(namedactors)andtheircommunication.TheideabehindCalvinposessimilaritywiththeserverlessarchitectureandcanbeseenasActorasaServiceinsteadofFunctionasaService.ThismakesCalvinverypowerfulasitdoesnotonlyscaleactorsquicklybutalsoprovidesaneasyactormigrationcapability.Inthiswork,weproposeCalvinConstrained,anextensiontotheCalvinframeworktocoverresource-constraineddevices.Duetolimitedmemoryandprocessingpowerofembeddeddevices,theconstrainedsideoftheframeworkcanonlysupportalimitedsubsetoftheCalvinfeatures.ThecurrentimplementationofCalvinConstrainedsupportsactorsimplementedinC

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aswellasPython,wherethesupportforPythonactorsisenabledbyusingMicroPythonasastaticallyallocatedlibrary,bythisweenabletheautomaticmanagementofstatevariablesandenhancecodere-usability.Aswouldbeexpected,Python-codedactorsdemandmoreresourcesoverC-codedones.Weshowthattheextraresourcesneededaremanageableoncurrentoff-the-shelvemicro-controller-equippeddeviceswhenusingtheCalvinframework.

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Applications and Experiences Track Paper Abstracts

Application1:Security,Privacy,TrustinDistributedSystemsPrivacyPreservingUser-basedRecommenderSystem ShahriarBadsha(RMITUniversity),XunYi(RMITUniversity),IbrahimKhalil(RMITUniversity),ElisaBertino(PurdueUniversity)

Withtherapiddevelopmentofthesocialnetworks,CollaborativeFiltering(CF)-basedrecommendersystemshavebeenincreasinglyprevalentandbecomewidelyacceptedbyusers.TheCF-basedtechniquesgeneraterecommendationsbycollectingprivacysensitivedatafromusers.Usually,theusersaresensitivetodisclosureofpersonalinformationand,consequently,thereareunavoidablesecurityconcernssinceprivateinformationcanbeeasilymisusedbymaliciousthirdparties.Inordertoprotectagainstbreachesofpersonalinformation,itisnecessarytoobfuscateuserinformationbymeansofanefficientencryptiontechniquewhilesimultaneouslygeneratingtherecommendationbymakingtrueinformationinaccessibletoserviceproviders.Therefore,weproposeaprivacypreservinguser-basedCFtechniquebasedonhomomorphicencryption,whichiscapableofdeterminingsimilaritiesamongusersfollowedbygeneratingrecommendationswithoutrevealinganyprivateinformation.Weintroducedifferentsemi-honestpartiestopreserveprivacyandtocarryoutintermediatecomputationsforgeneratingrecommendations.Weimplementourmethodonpubliclyavailabledatasetsandshowthatourmethodispracticalaswellasachieveshighlevelofsecurityforuserswithoutcompromisingtherecommendationaccuracy.

PrivacyPreservingOptimizationofParticipatorySensinginMobileCloudComputing YeYan(OaklandUniversity),DongHan(OaklandUniversity),TaoShu(AuburnUniversity)

Withtherapidgrowthofmobilecloudcomputing,participatorysensingemergesasanewparadigmtoexploreourphysicalworldatanunprecedentedfinegranularitybyrecruitingthepervasivesensor-enabledsmartphones.Whileextensiveoptimizationhasbeenperformedintheliteraturetocoordinatethesensingactivityofthecloud-basedsensingserver(orplatform)andtheparticipatingsmartphonessoastomaximizetheefficiencyofparticipatorysensing,theprivacyissueintheoptimizationhasbeenlargelyoverlooked.Inthispaper,weproposeanovelprivacy-preservingoptimizationframeworkthatallowsboththecloud-basedplatformandmobileuserstosharedatafortheformulationandsolutionoftheoptimization,butwithoutrevealingsensitiveinformationthatmayleadtoprivacyleakageofeachother.Ourmethodisbuiltuponaprivacy-preservingversionofthewell-knownNP-hardweightedset-coverageproblem.Toaccommodateprivacyrequirementsinthisframework,oursolutionusesamodifiedbloomfilteralongwithaDiffie-Hellman-typeexchangeprotocolamongallparticipantsfordataaggregation,sharing,andpresentation.Throughextensivesimulationweevaluatetheprivacystrengthoftheproposedapproachandalsoverifyitseffectivenessandlowoverhead.

SPHINX:APasswordStorethatPerfectlyHidesPasswordsfromItself MalihehShirvanian(UniversityofAlabamaatBirmingham),StanislawJarecki(UniversityofCaliforniaatIrvine),HugoKrawczyk(IBMResearch),NiteshSaxena(UniversityofAlabamaatBirmingham)

Passwordmanagers(akastoresorvaults)allowausertostoreandretrieve(usuallyhigh-entropy)passwordsforhermultiplepassword-protectedservicesbyinteractingwitha“device”servingtheroleofthemanager(e.g.,asmartphoneoranonlinethird-partyservice)onthebasisofasinglememorable(low-entropy)masterpassword.Existingpasswordmanagersworkwelltodefeatofflinedictionaryattacksuponwebservicecompromise,assumingtheuseofhigh-entropypasswordsisenforced.However,theyarevulnerabletoleakageofallpasswordsintheeventthedeviceiscompromised,duetotheneedtostorethepasswordsencryptedunderthemasterpasswordand/ortheneedtoinputthemasterpasswordtothedevice(asinsmartphonemanagers).Evidenceexiststhatpasswordmanagerscanbeattractiveattacktargets.Inthispaper,weintroduceanovelapproachtopasswordmanagement,calledSPHINX,whichremainssecureevenwhenthepasswordmanageritselfhasbeencompromised.InSPHINX,theinformationstoredonthedeviceisinformationtheoreticallyindependentoftheuser’smasterpassword—anattackerbreakingintothedevicelearnsnoinformationaboutthemasterpasswordortheuser’ssite-specificpasswords.Moreover,anattackerwithfullcontrolofthedevice,evenatthetimetheuserinteractswithit,learnsnothingaboutthemasterpassword—thepasswordisnotenteredintothedeviceinplaintextformorinanyotherwaythatmayleakinformationonit.Unlikeexistingmanagers,SPHINXproducesstrictlyhigh-entropypasswordsandmakesitcompulsoryfortheuserstoregistertheserandomizedpasswordswiththewebservices,hencefullydefeatingofflinedictionaryattackuponservicecompromise.ThedesignandsecurityofSPHINXisbasedonthedevice-enhancedPAKEmodelofJareckietal.thatprovidesthetheoreticalbasisforthisconstructionandisbackedbyrigorouscryptographicproofsofsecurity.WhileSPHINXissuitablefordifferentdeviceandonlineplatforms,inthispaper,wereportonitsconcreteinstantiationonsmartphonesgiventheirpopularityandtrustworthinessaspasswordmanagers(oreventwo-factorauthentication).Wepresentthedesign,implementationandperformanceevaluationofSPHINX,offeringprototypebrowserplugins,smartphoneappsandtransparentdevice-clientcommunication.Basedonourinspectionanalysis,theoveralluserexperienceofSPHINXimprovesuponcurrentmanagers.Wealsoreportonalab-basedusabilitystudyofSPHINX,whichindicatesthatusers’perceptionofSPHINXsecurityandusabilityishighandsatisfactorywhencomparedtoregularpassword-basedauthentication.Finally,wediscusshowSPHINXmaybeextendedtoanonlineserviceforthepurposeofback-uporasanindependentpasswordmanager.

WhenSmartTVMeetsCRN:Privacy-preservingFine-grainedSpectrumAccess ChaowenGuan(StateUniversityofNewYorkatBuffalo),AzizMohaisen(StateUniversityofNewYorkatBuffalo),ZhiSun(StateUniversityofNewYorkatBuffalo),LuSu(StateUniversityofNewYorkatBuffalo),KuiRen(StateUniversityofNewYorkatBuffalo),YalingYang(VirginiaTech)

DynamicspectrumsharingtechniquesappliedintheUHFTVbandhavebeendevelopedtoallowsecondaryWiFitransmissioninareaswithactiveTVusers.Thistechniqueofdynamicallycontrollingtheexclusionzoneenablesvastlyincreasingsecondaryspectrumre-use,comparedtothe``TVwhitespace''modelwhereTVtransmittersdeterminetheexclusionzoneandonly"idle"channelscanbere-purposed.However,incurrentsuchdynamicspectrumsharingsystems,thesensitiveoperationparametersofbothprimaryTVusers(PUs)andsecondaryusers(SUs)needtobesharedwiththespectrumdatabasecontroller(SDC)forthepurposeofrealizingefficientspectrumallocation.SincesuchSDCserverisnotnecessarilyoperatedbyatrustedthirdparty,thosecurrentsystemsmightcauseessentialthreatenstotheprivacyrequirementfrombothPUsandSUs.Toaddressthisprivacyissue,thispaperproposesaprivacy-preservingspectrumsharingsystembetweenPUsandSUs,whichrealizesthespectrumallocationdecisionprocessusingefficientmulti-partycomputation(MPC)technique.Inthisdesign,theSDConlyperformssecurecomputationoverencryptedinputfromPUsandSUssuchthatnoneofthePUorSUoperationparameterswillberevealedtoSDC.TheevaluationofitsperformanceillustratesthatourproposedsystembasedonefficientMPCtechniquescanperformdynamicspectrumallocationprocessbetweenPUsandSUsefficientlywhilepreservingusers'privacy.

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RevisitingSecurityRisksofAsymmetricScalarProductPreservingEncryptionandItsVariants WeipengLin(SimonFraserUniveristy),KeWang(SimonFraserUniversity),ZhilinZhang(SimonFraserUniversity),HongChen(RenminUniversityofChina)

Cloudcomputinghasemergedasacompellingvisionformanagingdataanddeliveringqueryansweringcapabilityovertheinternet.Thisnewwayofcomputingalsoposesarealriskofdisclosingconfidentialinformationtothecloud.Searchableencryptionaddressesthisissuebyallowingthecloudtocomputetheanswertoaquerybasedontheciphertextsofdataandqueries.Thankstoitsinnerproductpreservationproperty,theasymmetricscalar-product-preservingencryption(ASPE)hasbeenadoptedandenhancedinagrowingnumberofworkstoperformavarietyofqueriesandtasksinthecloudcomputingsetting.However,thesecuritypropertyofASPEanditsenhancedschemeshasnotbeenstudiedcarefully.Inthispaper,weshowacompletedisclosureofASPEandseveralpreviouslyunknownsecurityrisksofitsenhancedschemes.Meanwhile,efficientalgorithmsareproposedtolearntheplaintextofdataandqueriesencryptedbytheseschemeswithlittleornoknowledgebeyondtheciphertexts.Wedemonstratetheserisksonrealdatasets.

AnAdversary-CentricBehaviorModelingofDDoSAttacks AnWang(GeorgeMasonUniversity),AzizMohaisen(SUNYBuffalo),SongqingChen(GeorgeMasonUniversity)

DistributedDenialofService(DDoS)attacksaresomeofthemostpersistentthreatsontheInternettoday.TheevolutionofDDoSattackscallsforanin-depthanalysisofthoseattacks.Abetterunderstandingoftheattackers’behaviorcanprovideinsightstounveilpatternsandstrategiesutilizedbyattackers.Thepriorartontheattackers’behavioranalysisoftenfallsintwoaspects:itassumesthatadversariesarestatic,andmakescertainsimplifyingassumptionsontheirbehavior,whichoftenarenotsupportedbyrealattackdata.Inthispaper,wetakeadata-drivenapproachtodesigningandvalidatingthreeDDoSattackmodelsfromtemporal(e.g.,attackmagnitudes),spatial(e.g.,attackerorigin),andpatiotemporal(e.g.,attackinter-launchingtime)perspectives.Wedesignthesemodelsbasedontheanalysisoftracesconsistingofmorethan50,000verifiedDDoSattacksfromindustrialmitigationoperations.EachmodelisalsovalidatedbytestingitseffectivenessinaccuratelypredictingfutureDDoSattacks.ComparisonsagainstsimpleintuitivemodelsfurthershowthatourmodelscanmoreaccuratelycapturetheessentialfeaturesofDDoSattacks.

Application2:SocialNetworksandCrowdsourcingAnti-MaliciousCrowdsourcingUsingtheZero-DeterminantStrategy QinHu(BeijingNormalUniversity),ShenglingWang(BeijingNormalUniversity),LiranMa(TexasChristianUniversity),RongfangBie(BeijingNormalUniversity),XiuzhenCheng(GeorgeWashingtonUniversity)

Crowdsourcingisapromisingparadigmtoaccomplishacomplextaskviaelicitingservicesfromalargegroupofcontributors.However,recentobservationsindicatethatthesuccessofcrowdsourcingisbeingthreatenedbythemaliciousbehaviorsofthecontributors.Inthispaper,weanalyzethemaliciousattackproblemusinganiteratedprisoner’sdilemma(IPD)gameandproposeazero-determinant(ZD)strategybasedschemebyrewardingaworker’scooperationorpenalizingthedefectionforenticinghisfinalcooperation.Boththeoreticalanalysisandsimulationstudyindicatethattheproposedalgorithmhastwoattractivecharacteristics:1)therequestorcanincentivizetheworkertokeeponcooperatingbyonlyincreasingtheshort-termpayment;and2)theproposedalgorithmisfair,sotherequestorcannotarbitrarilypenalizeaninnocentworkertoincreaseherpayoffeventhoughshecandominatethegame.Tothebestofourknowledge,wearethefirsttousetheZDstrategytostimulatebothplayerstocooperateinanIPDgame.Moreover,ourproposedalgorithmisnotrestrictedtosolvetheproblemofthemaliciouscrowdsourcing-itcanbeemployedtotackleanyproblemthatcanbeformulatedbyanIPDgame.

JPR:ExploringJointPartitioningandReplicationforTrafficMinimizationinOnlineSocialNetworks JingyaZhou(SoochowUniversity),JianxiFan(SoochowUniversity)

Ascalablestoragesystembecomesmoreimportanttodayforonlinesocialnetworks(OSNs)asthevolumeofuserdataincreasesrapidly.Key-valuestoreusesconsistenthashingtosavedatainadistributedmanner.Asadefactostandard,ithasbeenwidelyusedinproductionenvironmentsofmanyOSNs.However,therandomnatureofhashingalwaysleadstohighinter-servertraffic.Recently,partitioningandreplicationarerespectivelyproposedinmanyexistingworkswheretheformeraimstominimizetheinter-serverreadtrafficandthelatteraimstooptimizetheinter-serverwritetraffic.Nevertheless,theseparatedmannersofoptimizationcannotefficientlyreducethetraffic.Becausetheinter-serverreadtrafficischangedduringreplication.Inthispaper,wesuggestthatperformingpartitioningandreplicationsimultaneouslycouldprovideprobabilitytofurtheroptimizetraffic.Thenweformulatetheproblemasarevisedgraphpartitioningwithoverlaps,sinceoverlapspartitioningnaturallycorrespondstoreplication.Tosolvetheproblem,weproposeaJointPartitioningandReplication(JPR)scheme.ThroughextensiveexperimentswitharealworldFacebooktrace,weevaluatethatJPRsignificantlyreducesinter-servertrafficwithslightlysacrificingstoragecostcomparedtohashing,andpreservesagoodloadbalancingacrossserversaswell.

OptimizingSourceSelectioninSocialSensinginthePresenceofInfluenceGraphs HuajieShao(UIUC),ShiguangWang(UIUC),ShenLi(UIUC),ShuochaoYao(UIUC),YiranZhao(UIUC),MdTanvirAlAmin(UIUC),TarekAbdelzaher(UIUC),LanceKaplan(UIUC)

Thispaperaddressestheproblemofchoosingtherightsourcestosolicitdatafrominsensingapplicationsinvolvingbroadcastchannels,suchasthosecrowdsensingapplicationswheresourcessharetheirobservationsonsocialmedia.Thegoalistoselectsourcessuchthatexpectedfusionerrorisminimized.Weassumethatsolicitingdatafromasourceincursacostandthatthecostbudgetislimited.Contrarytootherformulationsofthisproblem,wefocusonthecasewheresomesourcesinfluenceothers.Hence,askingasourcetomakeaclaimaffectsthebehaviorofothersourcesaswell,accordingtoaninfluencemodel.Thepapermakestwocontributions.First,wedevelopananalyticmodelforestimatingexpectedfusionerror,givenaparticularinfluencegraphandsolutiontothesourceselectionproblem.Second,weusethatmodeltosearchforasolutionthatminimizesexpectedfusionerror,formulatingitasazero-oneintegernon-linearprogramming(INLP)problem.Toscaletheapproach,thepaperfurtherproposesanovelreliability-basedpruningheuristic(RPH)andasimilarity-basedlossyestimation(SLE)algorithmthatsignificantlyreducethecomplexityoftheINLPalgorithmatthecostofamodestapproximation.Theanalyticallycomputedexpectedfusionerrorisvalidatedusingbothsimulationsandreal-worlddatafromTwitter,demonstratingagoodmatchbetweenanalyticpredictionsandempiricalmeasurements.Itisalsoshownthatourmethodoutperformsbaselinesintermsofresultingfusionerror.

DynamicContractDesignforHeterogenousWorkersinCrowdsourcingforQualityControl ChenxiQiu(PennsylvaniaStateUniversity),AnnaSquicciarini(PennsylvaniaStateUniversity),SarahRajtmajer(PennsylvaniaStateUniversity),JamesCaverlee(TexasA&MUniversity)

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Crowdsourcingsitesheavilyrelyonpaidworkerstoensurecompletionoftasks.Yet,designingapricingstrategiesabletoincentivizeusers’qualityandretentionisnontrivial.Existingpaymentstrategieseithersimplysetafixedpaymentpertaskwithoutconsideringchangesinworkers’behaviors,orruleoutpoorqualityresponsesandworkersbasedoncoarsecriteria.Hence,taskrequestersmaybeinvestingsignificantlyinworkthatisinaccurateorevenmisleading.Inthispaper,wedesignadynamiccontracttoincentivizehigh-qualitywork.Ourproposedapproachoffersatheoreticallyprovenalgorithmtocalculatethecontractforeachworkerinacost-efficientmanner.Incontrasttoexistingwork,ourcontractdesignisnotonlyadaptivetochangesinworkers’behavior,butalsoadjustspricingpolicyinthepresenceofmaliciousbehavior.BoththeoreticalandexperimentalanalysisoverrealAmazonreviewtracesshowthatourcontractdesigncanachieveanearoptimalsolution.Furthermore,experimentalresultsdemonstratethatourcontractdesign1)canpromotehigh-qualityworkandpreventmaliciousbehavior,and2)outperformstheintuitivestrategyofexcludingallmaliciousworkersintermsoftherequester’sutility.

JointRequestBalancingandContentAggregationinCrowdsourcedCDN MingMa(TsinghuaUniversity),ZhiWang(TsinghuaUniversity),KunYi(TsinghuaUniversity),JiangchuanLiu(SouthChinaAgriculturalUniversity),LifengSun(TsinghuaUniversity)

RecentyearshavewitnessedanewcontentdeliveryparadigmnamedcrowdsourcedCDN,inwhichdevicesdeployedatedgenetworkcanprefetchcontentsandprovidecontentdeliveryservice.CrowdsourcedCDNoffershigh-qualityexperiencetoend-usersbyreducingtheircontentaccesslatencyandalleviatestheloadofnetworkbackbonebymakinguseofnetworkandstorageresourcesatmillionsofedgedevices.Insuchparadigm,redirectingcontentrequeststoproperdevicesiscriticalforuserexperience.TheuniquenessofrequestredirectioninsuchcrowdsourcedCDNliesthat:ononehand,thebandwidthcapacityofthecrowdsourcedCDNdevicesislimit,hencedeviceslocatedatacrowdedplacecanbeeasilyoverwhelmedwhenservingnearbyuserrequests;ontheotherhand,contentsrequestedinonedevicecanbesignificantlydifferentfromanotherone,makingrequestredirectionstrategiesusedinconventionalCDNswhichonlyaimtobalancerequestloadsineffective.Inthispaper,weexplorerequestredirectionstrategiesthattakebothworkloadbalanceofdevicesandcontentrequestedbyusersintoconsideration.Ourcontributionsareasfollows.First,weconductmeasurementstudies,coving1.8Muserswatching0.4Mvideos,tounderstandrequestpatternsincrowdsourcedCDN.Weobservethattheloadsofnearbydevicescanbeverydifferentandthecontentsrequestedatnearbydevicescanalsobesignificantlydifferent.Theseobservationsleadtoourdesignforrequestbalancingatnearbydevices.Second,weformulatetherequestredirectionproblembytakingboththecontentaccesslatencyandthecontentreplicationcostintoconsideration,andproposearequestbalancingandcontentaggregationsolution.Finally,weevaluatetheperformanceofourdesignusingtrace-drivensimulations,andobserveourschemeoutperformsthetraditionalstrategyintermsofmanymetrics,e.g.,weobserveacontentaccesslatencyreductionby50%overtraditionalmechanismssuchastheNearest/Randomrequestroutingscheme.

Shrink:ABreastCancerRiskAssessmentModelBasedonMedicalSocialNetwork AliLi(UniversityofScienceandTechnologyBeijing),RuiWang(UniversityofScienceandTechnologyBeijing),LeiXu(UniversityofScienceandTechnologyBeijing)

Breastcancerriskassessmentmodelcanassesswhetherapeopleisatahighriskofdevelopingbreastcancerdiseaseornotandconfirmabreastcancerhigh-riskgroup.Becausetheetiologyofbreastcancerdiseaseisdifferentindifferentcountryandregion,theexistingriskassessmentmodelisonlyadaptivetocertaincountriesandregions.Andtheparametersofthesemodelsarefixed,sothesemodelshavepoorgenerality.Aimingattheseproblems,thepaperputsforwardanewbreastcancerriskassessmentmodelnamedasShrink.Usingtheideaofsocialnetwork,Shrinkconstructsamedicalsocialnetworktoshowthesimilarityamongpeople,andusesgroupdivisionalgorithmtodividethenetworkintobreastcancerhigh-riskgroupandlow-riskgroup.Theparametersofthismodelcanbesetaccordingtotheneedsofthebreastcensus,andtheseparameterscanbedirectlyacquiredthroughquestionnaire,thereforeShrinkhasgoodgenerality.Moreover,undertheuncertainclassificationstandard,Shrinkadoptsanewclassificationmethodtodiscoverbreastcancerhigh-riskgroup.Basedontherealdatafromquestionnaires,wemakeexperimentsinMatlab,andobtaintheevaluationindexofthemodel.TheexperimentprovesthatthemodelitselfhasgoodevaluationresultandisbetterthanclassicGailmodel.

Application3:InternetofThings,SmartCities,andCyber-PhysicalSystemsOpportunisticEnergySharingBetweenPowerGridandElectricVehicles:AGameTheory-BasedPricingPolicy AnkurSarker(UniversityofVirginia),ZhuozhaoLi(UniversityofVirginia),WilliamKolodzey(ClemsonUniversity),HaiyingShen(UniversityofVirginia)

Electricvehicles(EVs)havegreatpotentialtoreducedependencyonfossilfuels.TherecentsurgeinthedevelopmentofonlineEV(OLEV)willhelptoaddressthedrawbacksassociatedwithcurrentgenerationEVs,suchastheheavyandexpensivebatteries.OLEVsareintegratedwiththesmartgridofpowerinfrastructurethroughawirelesspowertransfersystem(WPT)toincreasethedrivingrangeoftheOLEV.However,theintegrationofOLEVswiththegridcreatesatremendousloadforthesmartgrid.Thedemandofapowergridchangesovertimeandthepriceofpowerisnotfixedthroughouttheday.ThereshouldbesomecongestionavoidanceandloadbalancingpolicyimplicationstoensurequalityofservicesforOLEVs.Inthispaper,first,weconductananalysistoshowtheexistenceofunpredictablepowerloadandcongestionbecauseofOLEVs.WeusetheSimulationforUrbanMObilitytoolandhourlytrafficcountsofaroadsectionoftheNewYorkCitytoanalyzetheamountofenergyOLEVscanreceiveatdifferenttimesoftheday.Then,wepresentagametheorybasedonadistributedpowerscheduleframeworktofindtheoptimalschedulebetweenOLEVsandsmartgrid.Intheproposedframework,OLEVsreceivetheamountofpowerchargingfromthesmartgridbasedonapowerpaymentfunctionwhichisupdatedusingbestresponsestrategy.Weprovethattheupdatedpowerrequestsconvergetotheoptimalpowerschedule.Inthisway,thesmartgridmaximizesthesocialwelfareofOLEVs,whichisdefinedasmixedconsiderationoftotalsatisfactionanditspowerchargingcost.Finally,weverifytheperformanceofourproposedpricingpolicyunderdifferentscenariosinasimulationstudy.

EnergyEfficientObjectDetectioninCameraSensorNetworks TuanDao(UCRiverside),KarimKhalil(UCRiverside),AmitRoy-Chowdhury(UCRiverside),SrikanthKrishnamurthy(UCRiverside),LanceKaplan(U.S.ArmyResearchLaboratory)

Awirelesscameranetworkcanprovidesituationawarenessinformation(e.g.,humansindistress)inscenariossuchasdisasterrecovery.Ifsuchcamerasensorsarebatteryoperated,sendingrawvideofeedsbacktoacentralcontrollercanbeexpensiveintermsofenergyconsumption.Further,ifallcamerasweretousetheoptimalprocessingalgorithmforobjectdecision,theymayalsoexpendunnecessaryenergy.Statedotherwise,camerasthatcapturethesameobjectsmaynotallhavetousetheoptimalalgorithmtoachieveadesiredaccuracy,andthiscansaveprocessingenergycosts.Inthispaper,ourobjectiveistodesignandimplementaframeworkthatcansupportcoordinationamongcamerastodeliverhighlyaccuratedetectionofobjectsinanenergyefficientway.Theframework,whichwecallEECS(forenergyefficientcamerasensors),estimatesthedetectionaccuracyandenergycostsincurred(boththeprocessingandcommunicationcostsaretakeninto

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account)witheachdetectionalgorithmforeachcamera,andcomesupwithachoiceofcamerasforsendinginformationpertainingtotheobjectofinterest.Thissetofcamerasandthevideoprocessingalgorithmsthattheymustuse,arechosensoastominimizetheenergyexpenditures,givenadesireddetectionaccuracy.WeimplementEECSonacameranetworkbuiltwithsmartphones,anddemonstratethatitreducestheenergyconsumptionbyupto40%whileensuringaobjectdetectionaccuracyofover86%.

DeepOpp:Context-awareMobileAccesstoSocialMediaContentonUndergroundMetroSystems DiWu(HunanUniversity&ImperialCollegeLondon),DmitriArkhipov(UniversityofCaliforniaIrvine),ThomasPrzepiorka(ImperialCollegeLondon),QiangLiu(DartmouthCollege),JulieMcCann(ImperialCollegeLondon),AmeliaRegan(UniversityofCaliforniaIrvine)

Accessingonlinesocialmediacontentonundergroundmetrosystemsisachallengeduetothefactthatpassengersoftenloseconnectivityforlargepartsoftheircommute.Astheoldestmetrosystemintheworld,theLondonundergroundrepresentsatypicaltransportationnetworkwithintermittentInternetconnectivity.Todealwithdisruptioninconnectivityalongthesub-surfaceanddeep-levelundergroundlinesontheLondonunderground,wehavedesignedacontext-awaremobilesystemcalledDeepOppthatenablesefficientofflineaccesstoonlinesocialmediabyprefetchingandcachingcontentopportunisticallywhensignalavailabilityisdetected.DeepOppcanmeasure,crowdsourceandpredictsignalcharacteristicssuchasstrength,bandwidthandlatency;itcanusethesepredictionsofmobilenetworksignaltoactivateprefetching,andthenemployanoptimizationroutinetodeterminewhichsocialcontentshouldbecachedinthesystemgivenreal-timenetworkconditionsanddevicecapacities.DeepOpphasbeenimplementedasanAndroidapplicationandtestedontheLondonUnderground;itshowssignificantimprovementoverexistingapproaches,e.g.reducingtheamountofpowerneededtoprefetchsocialmediaitemsby2.5times.WhileweusetheLondonUndergroundtotestoursystem,itisequallyapplicableinNewYork,Paris,Madrid,Shanghai,oranyotherurbanundergroundmetrosystem,orindeedinanysituationinwhichusersexperiencelongbreaksinconnectivity.

PhaseBeat:ExploitingCSIPhaseDataforVitalSignMonitoringwithCommodityWiFiDevices XuyuWang(AuburnUniversity),ChaoYang(AuburnUniversity),ShiwenMao(AuburnUniversity)

Vitalsigns,suchasrespirationandheartbeat,areusefultohealthmonitoringsincesuchsignalsprovideimportantcluesofmedicalconditions.Effectivesolutionsareneededtoprovidecontact-free,easydeployment,low-cost,andlong-termvitalsignmonitoring.Inthispaper,wepresentPhaseBeattoexploitchannelstateinformation(CSI)phasedifferencedatatomonitorbreathingandheartbeatwithcommodityWiFidevices.WeprovidearigorousanalysisoftheCSIphasedifferencedatawithrespecttoitsstabilityandperiodicity.Basedontheanalysis,wedesignandimplementthePhaseBeatsystemwithoff-the-shelfWiFidevices,andconductanextensiveexperimentalstudytovalidateitsperformance.OurexperimentalresultsdemonstratethesuperiorperformanceofPhaseBeatoverexistingapproachesinvariousindoorenvironments.

REX:RapidEnsembleClassificationSystemforLandslideDetectionusingSocialMediaAibekMusaev(UniversityofAlabama),DeWang(GeorgiaInstituteofTechnology),JiatengXie(GeorgiaInstituteofTechnology),CaltonPu(GeorgiaInstituteofTechnology)

WestudytheproblemofusingSocialMediatodetectnaturaldisasters,ofwhichweareinterestedinaspecialkind,namelylandslides.EmployinginformationfromSocialMediapresentsuniqueresearchchallenges,asthereexistsaconsiderableamountofnoiseduetomultiplemeaningsofthesearchkeywords,suchas``landslide"and``mudslide".Totacklethesechallenges,weproposeREX,arapidensembleclassificationsystemwhichcanfilteroutnoisyinformationbyimplementingtwokeyideas:(I)anewmethodforconstructingindependentclassifiersthatcanbeusedforrapidensembleclassificationofSocialMediatexts,whereeachclassifierisbuiltusingrandomizedExplicitSemanticAnalysis;and(II)aself-correctionapproachwhichtakesadvantageoftheobservationthatthemajoritylabelassignedtoSocialMediatextsbelongingtoalargeeventishighlyaccurate.WeperformexperimentsusingrealdatafromTwitterover1.5yearstoshowthatREXclassificationachieves0.98inF-measure,whichoutperformsthestandardBag-of-Wordsalgorithmbyanaverageof0.14andthestate-of-the-artWord2Vecalgorithmby0.04.Wealsoreleasetheannotateddatasetsusedintheexperimentsasacontributiontotheresearchcommunitycontaining282klabeleditems.

TowardAnIntegratedApproachtoLocalizingFailuresinCommunityWaterNetworksQingHan(UCIrvine),PhuNguyen(UCIrvine),RonaldT.Eguchi(ImageCat),Kuo-LinHsu(UCIrvine),NaliniVenkatasubramanian(UCIrvine)

Wepresentacyber-physical-humandistributedcomputingframework,AquaSCALE,forgathering,analyzingandlocalizinganomalousoperationsofincreasinglyfailure-pronecommunitywaterservices.Today,detectionofpipebreaks/leaksinwaternetworkstakeshourstodays.AquaSCALEleveragesdynamicdatafrommultipleinformationsourcesincludingIoT(InternetofThings)sensingdata,geophysicaldata,humaninput,andsimulation/modelingenginestocreateasensor-simulation-dataintegrationplatformthatcanaccuratelyandquicklyidentifyvulnerablespots.Weproposeatwo-phaseworkflowthatbeginswithrobustsimulationmethodsusingacommercialgradehydraulicsimulator-EPANET,enhancedwiththesupportforIoTsensorandpipefailuremodelings.Itgeneratesaprofileofanomalouseventsusingdiverseplug-and-playmachinelearningtechniques.Theprofilethenincorporateswithexternalobservations(NOAAweatherreportsandtwitterfeeds)torapidlyandreliablyisolatebrokenwaterpipes.Weevaluatethetwo-phasemechanismincanonicalandreal-worldwaternetworksunderdifferentfailurescenarios.Ourresultsindicatethattheproposedapproachwithofflinelearningandonlineinferencecanlocatemultiplesimultaneouspipefailuresatfinelevelofgranularity(individualpipelinelevel)withhighlevelofaccuracywithdetectiontimereducedbyordersofmagnitude(fromhours/daystominutes).

Application4:Mobile,Wireless,andEdgeComputingMobiQoR:PushingtheEnvelopeofMobileEdgeComputingviaQuality-of-ResultOptimizationYongboLi(GeorgeWashingtonUniversity),YurongChen(GeorgeWashingtonUniversity),TianLan(GeorgeWashingtonUniversity),GuruVenkataramani(GeorgeWashingtonUniversity)

Mobileedgecomputingaimsatimprovingapplicationresponsetimeandenergyefficiencybydeployingdataprocessingattheedgeofthenetwork.DuetotheproliferationofInternetofThingsandinteractiveapplications,theever-increasingdemandforlowlatencycallsfornovelapproachestofurtherpushingtheenvelopeofmobileedgecomputingbeyondexistingtaskoffloadinganddistributedprocessingmechanisms.Inthispaper,weidentifyanewtradeoffbetweenQuality-of-Result(QoR)andserviceresponsetimeinmobileedgecomputing.Ourkeyideaismotivatedbytheobservationthatagrowingsetofedgeapplicationsinvolvingmediaprocessing,machinelearning,anddataminingcantoleratesomelevelofqualitylossinthecomputedresult.ByrelaxingtheneedforhighestQoR,significantimprovementinserviceresponsetimecanbeachieved.Towardthisend,wepresentanoveloptimizationframework,MobiQoR,whichminimizesserviceresponsetimeandappenergyconsumptionbyjointlyoptimizingtheQoRofalledgenodesandtheoffloadingstrategy.TheproposedMobiQoRisprototypedusing

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Parse,anopensourcemobileback-endtool,onAndroidsmartphones.Usingrepresentativeapplicationsincludingfacerecognitionandmovierecommendation,ourevaluationwithreal-worlddatasetsshowsthatMobiQoRreducesresponsetimeandenergyconsumptionbyupto77%(infacerecognition)and189.3%(inmovierecommendation)overexistingstrategiesunderthesamelevelofQoRrelaxation.

TruthfulAuctionsforUserDataAllowanceTradinginMobileNetworks ZhongxingMing(TsinghuaUniversity),MingweiXu(TsinghuaUniversity),NingWang(SurreyUniversity),BeijeGao(TsinghuaUniversity),QiLi(TsinghuaUniversity)

Userdataallowancetradingemergesasapromisingpracticeinmobiledatanetworkssinceitcanhelpmobilenetworkstoattractmoreusers.However,todate,thereisnostudyonuserdataallowancetradinginmobilenetworks.Inthispaper,wedevelopatruthfulframeworkthatallowsuserstobidfordataallowance.Wefocusonpreventingpricecheating,guaranteeingfairness,andminimizingtradingmaintenancecostintrading.Weformulatethedatatradingprocessasadoubleauctionproblemanddevelopalgorithmstosolvetheproblem.Inparticular,weuseauniformpriceauctionbasedonacompetitiveequilibriumtodefendagainstpricecheatingandprovidefairness.Meanwhile,weleveragelinearprogrammingtominimizetradingmaintenancecost.Weconductextensivesimulationstodemonstratetheperformanceoftheproposedmechanism.Thesimulationresultsshowthatourtradingmechanismistruthfulandfair,whileincurringaminimizedmaintenancecost.

OnlineResourceAllocationforArbitraryUserMobilityinDistributedEdgeClouds LinWang(TUDarmstadt),LeiJiao(UniversityofOregon),JunLi(UniversityofOregon),MaxMühlhäuser(TUDarmstadt)

Ascloudsmovetothenetworkedgetofacilitatemobileapplications,edgecloudprovidersarefacingnewchallengesonresourceallocation.Asusersmaymoveandresourcepricesmayvaryarbitrarily,andservicedelaysareheterogeneous,resourcesinedgecloudsmustbeallocatedandadaptedcontinuouslyinordertoaccommodatesuchdynamics.Inthispaper,wefirstformulatethisproblemwithacomprehensivemodelthatcapturesthekeychallenges,thenintroduceagap-preservingtransformationoftheproblem,andproposeanovelonlinealgorithmthatoptimallysolvesaseriesofsubproblemswithacarefullydesignedlogarithmicobjective,finallyproducingfeasiblesolutionsforedgecloudresourceallocationovertime.Wefurtherproveviarigorousanalysisthatouronlinealgorithmcanprovideaparameterizedcompetitiveratio,withoutrequiringanyaprioriknowledgeoneithertheresourcepriceortheusermobility.Throughextensiveexperimentswithbothreal-worldandsyntheticdata,wefurtherconfirmtheeffectivenessoftheproposedalgorithm.Weshowthattheproposedalgorithmachievesnear-optimalresultswithanempiricalcompetitiveratioofabout1.1,reducesthetotalcostbyupto4xcomparedtostaticapproaches,andoutperformstheonlinegreedyone-shotoptimizationsbyupto70%.

LeveragingTargetk-CoverageinWirelessRechargeableSensorNetworks PengzhanZhou(StonyBrookUniversity),CongWang(StonyBrookUniversity),YuanyuanYang(StonyBrookUniversity)

Energyremainsamajorhurdleinrunningcomputation-intensivetasksonwirelesssensors.RecenteffortshavebeenmadetoemployaMobileCharger(MC)todeliverwirelesspowertosensors,whichprovidesapromisingsolutiontotheenergyproblem.MostofpreviousworksinthisareaaimatmaintainingperpetualnetworkoperationattheexpenseofhighoperatingcostofMC.Inthemeanwhile,itisobservedthatduetolowcostofwirelesssensors,theyareusuallydeployedathighdensitysothereisabundantredundancyintheircoverageinthenetwork.Forsuchnetworks,itispossibletotakeadvantageoftheredundancytoreducetheenergycost.Inthispaper,werelaxthestrictnessofperpetualoperationbyallowingsomesensorstotemporarilyrunoutofenergywhilestillmaintainingtargetk-coverageinthenetworkatlowercostofMC.Wefirstestablishatheoreticalmodeltoanalyzetheperformanceimprovementsunderthisnewstrategy.Thenweorganizesensorsintoload-balancedclustersfortargetmonitoringbyadistributedalgorithm.Next,weproposeachargingalgorithmnamedλ-GTSPChargingAlgorithmtodeterminetheoptimalnumberofsensorstobechargedineachclustertomaintaink-coverageinthenetworkandderivetherouteforMCtochargethem.Wefurthergeneralizethealgorithmtoencompassmobiletargetsaswell.OurextensivesimulationresultsdemonstratesignificantimprovementsofnetworkscalabilityandcostsavingthatMCcanextendchargingcapabilityover2-3timeswithareductionof40%ofmovingcostwithoutsacrificingthenetworkperformance.

ReducingCellularSignalingTrafficforHeartbeatMessagesviaEnergy-EfficientD2DForwarding YanqiJin(HuazhongUniversityofScience&Technology),FangmingLiu(HuazhongUniversityofScienceandTechnology),XiaomengYi(HuazhongUniversityofScience&Technology),MinghuaChen(TheChineseUniversityofHongKong)

MobileInstantMessaging(IM)apps,suchasWhatsAppandWeChat,frequentlysendheartbeatmessagestoremoteserverstomaintainalways-onlinestatus.Periodicheartbeatmessagesaresmallinsize,buttheirtransmissionsincurheavysignalingtraffictofrequentlyestablishandreleasecommunicationchannelsbetweenbasestationsandsmartphones,knownassignalingstorm.Meanwhile,smartphonesalsoneedtoactivatecellulardatacommunicationmodulefrequentlyfortransmittingshortheartbeatmessages,resultinginsubstantialenergyconsumption.Toaddresstheseissues,weproposeaDevice-to-Device(D2D)basedheartbeatrelayingframework,inordertoreducesignalingtrafficandenergyconsumptioninheartbeattransmission.Theframeworkselectsthesmartphonesasrelaystoopportunisticallycollectheartbeatmessagesfromnearbysmartphonesusingenergy-efficientD2Dcommunication.ThecollectedheartbeatmessagesaretransmittedtotheBSinanaggregatedmannertoreducecellularsignalingtraffic.Basedontheperiodsandtheexpirationtimeofthecollectedheartbeatmessages,theframeworkschedulesthetransmissionsofcollectedheartbeatmessagestominimizesignalingandenergyconsumptionwhilesatisfyingtimeconstrains.WeimplementandevaluateoursolutiononAndroidsmartphones.Theresultsfromreal-worldexperimentsshowthatoursolutionachievesmorethan50%signalingtrafficreductionandupto36%energysaving.

k-ProtectedRoutingProtocolinMulti-hopCognitiveRadioNetworks Chin-JungLiu(MichiganStateUniversity),LiXiao(MichiganStateUniversity)

Incognitiveradionetworks(CRNs),theestablishedcommunicationsessionsbetweensecondaryusers(SUs)maybeaffectedorevengetinterruptedbecausetheSUsneedtorelinquishthespectrumwhenthelicensedusers(PUs)appearandreclaimthespectrum/channel.OndetectingPUactivities,theSUsontheaffectedlinkseitherswitchtoanotheravailableidlespectrumusingthesamelinkortheSUsseekforanalternativepath/link.Ineitherapproach,theongoingsessionisdestinedtoexperiencedelayorevengetsinterrupted,whichisintolerabletoqualityofservice-sensitiveapplicationssuchasmultimediastreamingoraudio/videoconferencing.Inthispaper,westudytheproblemofestablishingk-protectedroutesinCRNs.Ak-protectedrouteconsistsofasetofmainlinkswithpreassignedbackupspectrumandbackuppathsandisguaranteedtosustainfromkPUappearanceswithoutbeinginterrupted.ForaCRN,wefindak-protectedrouteforeachsessionrequestandmaximizethenumberofsessionsthatcanbesupported.Weproposebothcentralizedanddistributedk-protectedroutingalgorithmsforthis

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problem.Simulationresultsshowthatourk-protectedroutingprotocoloutperformsexistingopportunisticspectrumswitchingapproachesintermsofdelayandinterruptionrate.

Application5:CloudComputingandDataCenterSystemsMulti-ResourceLoadBalancingforVirtualNetworkFunctions TaoWang(HuazhongUniversityofScience&Technology),HongXu(CityUniversityofHongKong),FangmingLiu(HuazhongUniversityofScienceandTechnology)

Middleboxesarewidelydeployedtoperformvariousnetworkfunctionstoensuresecurityandimproveperformance.TherecenttrendofNetworkFunctionVirtualization(NFV)makesiteasyforoperatorstodeploysoftwareimplementationsofthesenetworkfunctionsoncommodityservers.However,virtualnetworkfunctionsconsumedifferentamountsofresourceswhenprocessingpackets.Thusamulti-resourceloadbalancing(MRLB)mechanismisneededtoefficientlyutilizeserverresources.MRLBprobleminthecontextofNFVisfundamentallydifferentfrommulti-resourceallocationproblems,aswellastraditionalsingle-resourceloadbalancingandmulti-resourceloadbalancingproblemsintaskscheduling.Inthispaper,wetackletheMRLBprobleminNFVbyfirstproposingdominantload—theloadofthemoststressedresourceonaserver—astheloadbalancingmetric.WethenformulatetheMRLBproblemasanoptimizationtominimizethemaximumdominantloadofallNFVserversgiventhedemand.BasedonproximalJacobianADMM,weproposeanefficientalgorithmtosolvetheprobleminlargescalesettings.Throughextensivetrace-drivensimulationsandprototypeexperimentsonatestbed,weshowthatourMRLBalgorithmwithdominantloadperformssignificantlybetterandfasterthanbenchmarkingalgorithms.

Learningfromfailureacrossmultipleclusters:Atrace-drivenapproachtounderstanding,predicting,andmitigatingjobterminations NosaybaEl-Sayed(MIT),HongyuZhu(UniversityofToronto),BiancaSchroeder(UniversityofToronto)

Inlarge-scalecomputingplatforms,jobsarepronetointerruptionsandprematureterminations,limitingtheirusabilityandleadingtosignificantwasteinclusterresources.Inthispaper,wetacklethisprobleminthreesteps.First,weprovideacomprehensivestudybasedonlogdatafrommultiplelarge-scaleproductionsystemstoidentifypatternsinthebehaviourofunsuccessfuljobsacrossdifferentclustersandinvestigatepossiblerootcausesbehindjobtermination.Ourresultsrevealseveralinterestingpropertiesthatdistinguishunsuccessfuljobsfromothers,particularlyw.r.t.resourceconsumptionpatternsandjobconfigurationsettings.Secondly,wedesignamachinelearning-basedframeworkforpredictingjobandtaskterminations.Weshowthatjobfailurescanbepredictedrelativelyearlywithhighprecisionandrecall,andalsoidentifyattributesthathavestrongpredictivepowerofjobfailure.Finally,wedemonstrateinaconcreteusecasehowourpredictionframeworkcanbeusedtomitigatetheeffectofunsuccessfulexecutionusinganeffectivetask-cloningpolicythatwepropose.

RBAY:AScalableandExtensibleInformationPlaneforFederatingDistributedDatacenterResources XinChen(GeorgiaInstituteofTechnology),LitingHu(FloridaInternationalUniversity),DouglasM.Blough(GeorgiaInstituteofTechnology),MichaelA.Kozuch(IntelLabsPittsburgh),MatthewWolf(OakRidgeNationalLaboratory)

Whilemanyinstitutions,whetherindustrial,academic,orgovernmental,satisfytheircomputingneedsthroughpubliccloudproviders,manyothersstillmanagetheirownresources,oftenasgeographicallydistributeddatacenters.Sparecapacityfromthesegeographicallydistributeddatacenterscouldbeofferedtoothers,providedtherewereamechanismtodiscover,andthenrequesttheseresources.Unfortunately,singledatacenteradministratorstendnottocooperateduetoissuesofscalability,diverseadministrativepolicies,andsite-specificmonitoringinfrastructure.ThispaperdescribesRBAY,anintegratedinformationplanethatenablessecureandscalablesharingbetweengeographicallydistributeddatacenters.RBAY'skeydesignfeaturesaretwofold.First,RBAYemploysadecentralized`hierarchicalaggregationtree'structuretoseamlesslyaggregatespareresourcesfromgeographicallydistributeddatacenterstoaglobalinformationplane.Second,RBAYattachestoeachparticipatingservera`admin-customized'handler,whichfollowssite-specificpolicytoexpose,hide,add,removeresourcestoRBAY,andthusfulfillthetaskof`whichresourcetoexposetowhom,when,andhow'.Anexperimentalevaluationoneightreal-worldgeo-distributedsitesdemonstratesRBAY'srapidresponsetocompositequeries,aswellasitsextensible,scalable,andlightweightnature.

Task-awareTCPinDataCenterNetworks SenLiu(CentralSouthUniversity),JiaweiHuang(CentralSouthUniversity),YutaoZhou(CentralSouthUniversity),JianxinWang(CentralSouthUniversity),TianHe(UniversityofMinnesota)

Inmoderndatacenters,manyflow-basedandtask-basedschemeshavebeenproposedtospeedupthedatatransmissioninordertoprovidefast,reliableservicesformillionsofusers.However,existingflow-basedschemestreatallflowsinisolation,contributinglesstoorevenhurtinguserexperienceduetothestalledflows.Otherprevalenttask-basedapproaches,suchascentralizedanddecentralizedscheduling,aresophisticatedorunabletosharetaskinformation.Inthiswork,wefirstrevealthatrelinquishingbandwidthofleadingflowstothestalledoneseffectivelyreducesthetaskcompletiontime.Wefurtherpresentthedesignandimplementationofageneralsupportingschemethatsharestheflow-tardinessinformationthroughareceiver-drivencoordination.Ourschemecanbeflexiblyandwidelyintegratedwiththestate-of-the-artTCPprotocolsdesignedfordatacenters,whilemakingnomodificationonswitches.Throughthetestbedexperimentsandsimulationsoftypicaldatacenterapplications,weshowthatourschemereducesthetaskcompletiontimeby70%and50%comparedwiththeflow-basedprotocols(e.g.DCTCP,L2DCT)andtask-basedscheduling(e.g.Baraat),respectively.Moreover,ourschemealsooutperformsotherapproachesby18%to25%inprevalenttopologiesofdatacenter.

LimitationsofLoadBalancingMechanismsforN-TierSystemsinthePresenceofMillibottlenecks TaoZhu(GeorgiaInstituteofTechnology),JackLi(GeorgiaInstituteofTechnology),JoshKimball(GeorgiaInstituteofTechnology),JunheePark(IndianaUniversity),Chien-AnLai(GeorgiaInstituteofTechnology),CaltonPu(GeorgiaInstituteofTechnology),QingyangWang(LouisianaStateUniversity)

Thescalabilityofn-tiersystemsreliesoneffectiveloadbalancingtodistributeloadamongtheserversofthesametier.Wefoundthatloadbalancingmechanisms(andsomepolicies)inserversusedintypicaln-tiersystems(e.g.,ApacheandTomcat)haveissuesofinstabilitywhenverylongresponsetime(VLRT)requestsappearduetomillibottlenecks,veryshortbottlenecksthatlastonlytenstohundredsofmilliseconds.Experimentswithstandardn-tierbenchmarksshowthatduringmillibottlenecks,someloadbalancingpolicy/mechanismcombinationsmakethemistakeofsendingnewrequeststothenode(s)sufferingfrommillibottlenecks,insteadoftheidlenodesasloadbalancersaresupposedtodo.Severalofthesemistakesareduetotheimplicitassumptionsmadebyloadbalancingpoliciesand

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mechanismsonthestabilityofsystemstate.OurstudyshowsthatappropriateremediesatpolicyandmechanismlevelscanavoidthesemistakesduringmillibottlenecksandremovetheVLRTrequests,thusimprovingtheaverageresponsetimebyafactorof12.

PerformanceAnalysisofCloudComputingCentersServingParallelizableRenderingJobsUsingM/M/c/rQueuingSystems XiulinLi(ShandongUniversity),LiPan(ShandongUniversity),JiweiHuang(BeijingUniversityofPostsandTelecommunications),ShijunLiu(ShandongUniversity),YuliangShi(ShandongUniversity),CaltonPu(GeorgiaInstituteofTechnology)

Performanceanalysisiscrucialtothesuccessfuldevelopmentofcloudcomputingparadigm.Anditisespeciallyimportantforacloudcomputingcenterservingparallelizableapplicationjobs,fordeterminingaproperdegreeofparallelismcouldreducethemeanserviceresponsetimeandthusimprovetheperformanceofcloudcomputingobviously.Inthispaper,takingthecloudbasedrenderingserviceplatformasanexampleapplication,weproposeanapproximateanalyticalmodelforcloudcomputingcentersservingparallelizablejobsusingM/M/c/rqueuingsystems,bymodelingtherenderingserviceplatformasamulti-stationmulti-serversystem.Wesolvetheproposedanalyticalmodeltoobtainacompleteprobabilitydistributionofresponsetime,blockingprobabilityandotherimportantperformancemetricsforgivencloudsystemsettings.Thusthismodelcanguidecloudoperatorstodetermineapropersetting,suchasthenumberofservers,thebuffersizeandthedegreeofparallelism,forachievingspecificperformancelevels.Throughextensivesimulationsbasedonbothsyntheticdataandreal-worldworkloadtraces,weshowthatourproposedanalyticalmodelcanprovideapproximateperformancepredictionresultsforcloudcomputingcentersservingparallelizablejobs,eventhosejobarrivalsfollowdifferentdistributions.

Application6:BigDataSystemsandDistributedDataManagementandAnalyticsEvaluationofDeepLearningFrameworksoverDifferentHPCArchitectures ShayanShams(LouisianaStateUniversity),RichardPlatania(LouisianaStateuniversity),KisungLee(LouisianaStateUniversity),Seung-JongPark(LouisianaStateUniversity)

Recentadvancesindeeplearninghaveenabledresearchersacrossmanydisciplinestouncovernewinsightsaboutlargedatasets.Deepneuralnetworkshaveshownapplicabilitytoimage,time-series,textual,andotherdata,allofwhichareavailableinaplethoraofresearchfields.However,theircomputationalcomplexityandlargememoryoverheadrequiresadvancedsoftwareandhardwaretechnologiestotrainneuralnetworksinareasonableamountoftime.Tomakethispossible,therehasbeenaninfluxindevelopmentofdeeplearningsoftwarethataimtoleverageadvancedhardwareresources.Inordertobetterunderstandtheperformanceimplicationsofdeeplearningframeworksoverthesedifferentresources,weanalyzetheperformanceofthreedifferentframeworks,Caffe,TensorFlow,andApacheSINGA,overseveralhardwareenvironments.Thisincludesscalingupandoutwithsingle-andmulti-nodesetupsusingdifferentCPUandGPUtechnologies.Notably,weinvestigatetheperformancecharacteristicsofNVIDIA'sstate-of-the-arthardwaretechnology,NVLink,andalsoIntel'sKnightsLanding,themostadvancedIntelproductfordeeplearning,withrespecttotrainingtimeandutilization.Toourbestknowledge,thisisthefirstworkconcerningdeeplearningbench-markingwithNVLinkandKnightsLanding.Throughtheseexperiments,weprovideanalysisoftheframeworks'performanceoverdifferenthardwareenvironmentsintermsofspeedandscaling.Asaresultofthiswork,betterinsightisgiventowardsbothusinganddevelopingdeeplearningtoolsthatcatertocurrentandupcominghardwaretechnologies.

OnAchievingEfficientDataTransferforGraphProcessinginGeo-DistributedDatacenters AmelieChiZhou(InriaRennes),ShadiIbrahim(InriaRennes),BingshengHe(NationalUniversityofSingapore)

Graphpartitioningisimportantforoptimizingtheperformanceandcommunicationcostoflargegraphprocessingjobs.Recently,manygraphapplicationssuchassocialnetworksstoretheirdataongeo-distributeddatacenters(DCs)toprovideservicesworldwidewithlowlatency.Thisraisesnewchallengestoexistinggraphpartitioningmethods,duetothecostlyWideAreaNetwork(WAN)usageandthemulti-levelsofnetworkheterogeneitiesingeo-distributedDCs.Inthispaper,weproposeageo-awaregraphpartitioningmethodnamedG-Cut,whichaimsatminimizingtheinter-DCdatatransfertimeofgraphprocessingjobsingeo-distributedDCswhilesatisfyingtheWANusagebudget.G-CutadoptstwonoveloptimizationphaseswhichaddressthetwochallengesinWANusageandnetworkheterogeneitiesseparately.G-Cutcanbealsoappliedtopartitiondynamicgraphsthankstoitslight-weightruntimeoverhead.WeevaluatetheeffectivenessandefficiencyofG-Cutusingrealworldgraphswithbothrealgeo-distributedDCsandsimulations.EvaluationresultsshowthatG-Cutcanreducetheinter-DCdatatransfertimebyupto58%andreducetheWANusagebyupto70%comparedtostate-of-the-artgraphpartitioningmethodswithalowruntimeoverhead.

GBooster:TowardsAccelerationofGPU-intensiveMobileApplications ElliottWen(VictoriaUniversityofWellington),BryanNg(VictoriaUniversityofWellington),WinstonSeah(VictoriaUniversityofWellington),XueLiu(McGillUniversity),JiannongCao(TheHongKongPolytechnicUniversity),XuefengLiu(HuangzhongUniversityofScienceandTechnology)

TheperformanceofGPUsonmobiledevicesisgenerallythebottleneckofmultimediamobileapplications(e.g.,3Dgamesandvirtualreality).PreviousattemptstotackletheissuemainlymigrateGPUcomputationtoserversresidinginremotecloudcenters.However,thecostlynetworkdelayisespeciallyundesirableforhighly-interactivemultimediaapplicationssinceafastresponsetimeiscriticalforuserexperience.Inthispaper,weproposeGBooster,asystemthatacceleratesmultimediamobileapplicationsbytransparentlyoffloadingGPUtasksontoneighboringmultimediadevicessuchasSmartTVsandGamingConsoles.Specifically,GBoosterinterceptsandredirectssystemgraphicscallsbyutilizingtheDynamicLinkerHookingtechnique,whichrequiresnomodificationoftheapplicationsandthemobilesystems.Inaddition,amajorconcernforoffloadingisthehighenergyconsumptionincurredbynetworktransmissions.Toaddressthisconcern,GBoosterisdesignedtointelligentlyswitchbetweenthelow-powerBluetoothandthehigh-throughputWiFibasedonthetrafficdemand.WeimplementGBoosterontheAndroidsystemandevaluateitsperformance.Theresultsdemonstratethatitcanboostapplications'frameratesbyupto85%.Intermsofpowerconsumption,GBoostercanpreserveupto70%energycomparedwithlocalexecution.

Scalingk-NearestNeighborsQueries(Therightway) AtoshumSamuelCahsai(UniversityOfGlasgow),NikosNtarmos(UniversityOfGlasgow),ChristosAnagnostopoulos(UniversityofGlasgow),PeterTriantafillou(UniversityOfGlasgow)

Recentlyparallel/distributedprocessingapproacheshavebeenproposedforprocessingk-NearestNeighbours(kNN)queriesoververylarge(multidimensional)datasetsaimingtoensurescalability.However,thisistypicallyachievedattheexpenseofefficiency.Withthispaperweofferanovelapproachthatalleviatestheperformanceproblemsassociatedwithstateoftheartmethods.Theessenceofourapproach,whichdifferentiatesitfromrelatedresearch,restson(i)adoptingacoordinator-baseddistributedprocessingalgorithm,insteadofthoseemployedoverdata-parallelexecutionengines(suchasHadoop/MapReduceorSpark),and(ii)onawaytoorganizedata,tostructurecomputation,andtoindexthestoreddatasetsthatensuresthatonlyaverysmallnumberofdataitemsareretrievedfrom

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theunderlyingdatastore,communicatedoverthenetwork,andprocessedbythecoordinatorforeverykNNquery.Ourapproachalsopaysspecialattentiontoensuringscalabilityinadditiontolowqueryprocessingtimes.Overall,kNNqueriescanbeprocessedinjusttensofmilliseconds(asopposedtothe(tensof)secondsrequiredbystateoftheart.Wehaveimplementedourapproach,usingaNoSQLDB(HBase)asthedatastore,andwecompareitagainstthestate-of-the-art:theHadoop-basedSpatialHadoop(SHadoop)andtheSpark-basedSimbamethods.Weemploydifferentdatasetsofvarioussizes,showcasingthecontributedperformanceadvantages.Ourapproachoutperformsthestateoftheart,by2-3ordersofmagnitude,andconsistentlyfordatasetsizesrangingfromhundredsofmillionstohundredsofbillionsofdatapoints.Wealsoshowthatthekeyconstituentperformanceoverheadsincurredduringqueryprocessing(suchasthenumberofdataitemsretrievedfromthedatastore,therequirednetworkbandwidth,andtheprocessingtimeatthecoordinator)scaleverywell,ensuringtheoverallscalabilityoftheapproach.

ParallelizingBigDeBruijnGraphConstructiononHeterogeneousProcessors ShuangQiu(TheHongKongUniversityofScienceandTechnology),QiongLuo(TheHongKongUniversityofScienceandTechnology)

DeBruijngraphconstructionisthefirststepindenovoassemblerstoconnectinputreadsintoacompletesequencewithoutareferencegenome.Thisstepisbothtimeandmemoryspaceconsuming.Toaddressthisproblem,wedevelopParaHash,asystemthatpartitionstheinputdatainacompactformat,parallelizesthecomputationonboththeCPUsandtheGPUsinasinglecomputer,andperformshash-basedDeBruijngraphconstruction.Thisway,ParaHashutilizesallavailableprocessorstoassemblebiggenomesthatcannotfitintomemory.Furthermore,weanalyzethecharacteristicsofgenomedatatosetthehashtablesize,designconcurrenthashingalgorithmstohandletheinherentmultiplicity,andpipelinethedatatransferandthecomputationforfurtherefficiency.Ourexperimentsonreal-worldgenomedatasetsshowthattheworkloadwasbalancedacrossheterogeneousprocessors,andthatParaHashwasabletoconstructbillion-nodegraphsonasinglemachinewithanoverallperformanceupto20timesfasterthanthestate-of-the-artshared-memoryassemblers.

Private,yetPractical,MultipartyDeepLearning XinyangZhang(LehighUniversity),ShoulingJi(ZhejiangUniversity),HuiWang(StevensInstituteofTechnology),TingWang(LehighUniversity)

Inthispaper,weconsidertheproblemofmultipartydeeplearning(MDL),whereinautonomousdataownersjointlytrainaccuratedeepneuralnetworkmodelswithoutsharingtheirprivatedata.Wedesign,implement,andevaluate$\propto$MDL,anewMDLparadigmbuiltuponthreeprimitives:asynchronousoptimization,lightweighthomomorphicencryption,andthresholdsecretsharing.Comparedwithpriorwork,$\propto$MDLdepartsinsignificantways:a)besidesprovidingexplicitprivacyguarantee,itretainsdesirablemodelutility,whichisparamountforaccuracy-criticaldomains;b)itprovidesanintuitivehandlefortheoperatortogracefullybalancemodelutilityandtrainingefficiency;c)moreover,itsupportsdelicatecontrolovercommunicationandcomputationalcostsbyofferingtwovariants,operatingunderlooseandtightcoordinationrespectively,thusoptimizableforgivensystemsettings(e.g.,limitedversussufficientnetworkbandwidth).Throughextensiveempiricalevaluationusingbenchmarkdatasetsanddeeplearningarchitectures,wedemonstratetheefficacyof$\propto$MDL.

Application7:DistributedMiddlewareSystemsFastandFlexibleNetworkingforMessage-orientedMiddleware LarsKroll(KTHRoyalInstituteofTechnology),AlexandruA.Ormenisan(KTHRoyalInstituteofTechnology),JimDowling(KTHRoyalInstituteofTechnology)

Distributedapplicationsdeployedinmulti-datacenterenvironmentsneedtodealwithnetworkconnectionsofvaryingquality,includinghighbandwidthandlowlatencywithinadatacenterand,morerecently,highbandwidthandhighlatencybetweendatacentres.Inprinciple,foragivennetworkconnection,eachmessageshouldbesentoverthebestavailablenetworkprotocol,butexistingmiddlewaresdonotprovidethisfunctionality.Inthispaper,wepresentKompicsMessaging,amessagingmiddlewarethatallowsforfine-grainedcontrolofthenetworkprotocolusedonaper-messagebasis.Ratherthanalwaysrequiringapplicationdeveloperstospecifytheappropriateprotocolforeachmessage,wealsoprovideanonlinereinforcementlearnerthatoptimisestheselectionofthenetworkprotocolforthecurrentnetworkenvironment.Inexperiments,weshowhowconnectionproperties,suchasthevaryinground-triptime,influencetheperformanceoftheapplicationandweshowhowthroughputandlatencycanbeimprovedbypickingtherightprotocolattherighttime.

TailCut:PowerReductionunderQualityandLatencyConstraintsinDistributedSearchSystems Chih-HsunChou(UniversityofCalifornia,Riverside),LaxmiBhuyan(UniversityofCalifornia,Riverside),ShaoleiRen(UniversityofCalifornia,Riverside)

Websearchconstitutesanimportantclassofdataintensiveonlineservicesindatacenters.Optimizingsearchsystemsforenergyefficiency,timelyresponseandhighsearchquality(i.e.,howrelevantthereturnedresultsaretoasearchquery),however,isverychallenging,asasearchsysteminvolvesadistributedarchitecturewithhundredsofthousandsofindexservingnodes(ISNs)thatreturnsearchingresultstoanaggregatorthroughmultipleinterdependentretrievalstagesinapartition-aggregatefashion.Inthispaper,wediscoverthroughexperimentstwoimportantcharacteristicsthatcanaffectthesystemperformance:(1)responsetimeandenergyconsumptionaregreatlyimpactedbyasmallfractionofquerieswithlongprocessingtimes;(2)thequalitycontributionoftheISNisindependentofthequeryprocessingtime.Basedonourobservation,weproposeTailCut,whichjudiciouslydiscardslongqueryexecutionsandenablesISN-aggregatorcoordinationtominimizeenergyconsumptionsubjecttolatencyandqualityconstraints.OurexperimentalresultsshowthatTailCutcanachieveupto39%powersaving,whilesatisfyingthetaillatencyandqualityconstraint.

StoArranger:EnablingEfficientUsageofCloudStorageServicesonMobileDevices YongshuBai(SUNYBinghamton),YifanZhang(SUNYBinghamton)

Cloudstorageusagesarebecomingincreasinglypopularonmobiledevices.Throughanextensivemotivationstudy,wefindthatcloudstorageaccessesfrommobileappssufferfromseveralnotableproblemsthatundermineusageexperiences.Therootcauseisthatthewayofcloudstorageprovidersdeployingtheirservicesontomobiledevicesreliesonappdevelopersforthecorrectandappropriateimplementationsandlackstheabilityofmonitoringandservicingclient-sidecloudstorageaccesses.WeproposeStoArranger,apracticalsystemframeworkthatsolvestheproblemsbycoordinating,rearranging,andtransformingcloudstoragecommunicationsonmobiledevices.Wehaveprototypedtheproposedsystemusingtwodifferentimplementationapproaches.Wediscussourexperiencesoftheimplementationsinthepaper.Thereal-appevaluationexperimentsshowthatStoArrangercansignificantlyimprovemobilecloudstorageaccessefficiencywithlittleoverheads.

CharacterizingPerformanceandEnergy-efficiencyofTheRAMCloudStorageSystem

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YacineTaleb(Inria),ShadiIbrahim(Inria),GabrielAntoniu(Inria),ToniCortes(BarcelonaSupercomputingCenter)

Mostlargepopularwebapplications,likeFacebookandTwitter,havebeenrelyingonlargeamountsofin-memorystoragetocachedataandofferalowresponsetime.Asthemainmemorycapacityofclustersandcloudsincreases,itbecomespossibletokeepmostofthedatainthemainmemory.Thismotivatestheintroductionofin-memorystoragesystems.Whilepriorworkhasfocusedonhowtoexploitthelow-latencyofin-memoryaccessatscale,thereisverylittlevisibilityintotheenergy-efficiencyofin-memorystoragesystems.Eventhoughitisknownthatmainmemoryisafundamentalenergybottleneckincomputingsystems(i.e.,DRAMconsumesupto40%ofaserver’spower).Inthispaper,bythemeansofexperimentalevaluation,wehavestudiedtheperformanceandenergy-efficiencyofRAMCloud—awell-knownin-memorystoragesystem.WerevealthatalthoughRAMCloudisscalableforread-onlyapplications,itexhibitsnon-proportionalpowerconsumption.WealsofindthatthecurrentreplicationschemeimplementedinRAMCloudlimitstheperformanceandresultsinhighenergyconsumption.Surprisingly,weshowthatreplicationcanalsoplayanegativeroleincrash-recovery.

ProactivelySecureCloud-EnabledStorageKarimEldefrawy(HughesResearchLab),TylerKaczmarek(UniversityofCalifornia,Irvine),SkyFaber(UniversityofCalifornia,Irvine)

Attackingcloud-enabledstorageisbecomingincreasinglylucrativeasmorepersonalandenterprisedatamovestothecloud.Traditionalsecuritymechanismstemporarilylimitsuchattacks,butoveralongperiodoftimeattackerswilleventuallyfindvulnerabilities;thiscanleadtocompromisinglargeamountsofvaluabledataandleadtolarge-scaleprivacybreaches.Thispaperaddressesthisproblembyincorporatingproactivesecurityguaranteesintocloud-enabledstorage.Proactivesecuritydealswithanadversary’sabilitytoeventuallycompromiseallinvolvedserversinadistributedstorageorcomputationsystem.Whilethereareseveralproactivelysecuresecretsharingprotocolsthatcanbeusedtoimproveconfidentialityofdatastoredinthecloud,theirhighoverheadhastraditionallylimitedthemtolessthantenpartiesandtoonly100sofbytestypicalforcryptographickeys.Realizingproactivelysecurecloudstorageforlargerdata(e.g,MBs)requirescarefuldesignandcalibrationofsystemparameters,andfacesseveralchallenges.Inthispaperwedesign,implementandassessperformanceofthefirstsystemforProactivelySecureCloud-EnabledStorage(PiSCES)ofdatalargerthancryptographickeys.Basedonourpracticalperformanceresultsweadvocatethatthehighlevelofresilienceandlong-termsecurityandconfidentialityguaranteesenabledbyproactivesecurityshouldbeconsideredinfuturedistributedandcloud-basedstorageandcomputingservices.

BEES:Bandwidth-andEnergy-EfficientImageSharingforReal-timeSituationAwareness PengfeiZuo(HuazhongUniversityofScienceandTechnology),YuHua(HuazhongUniversityofScienceandTechnology),XueLiu(McGillUniverisity),DanFeng(HuazhongUniversityofScienceandTechnology),WenXia(HuazhongUniversityofScienceandTechnology),ShundeCao(HuazhongUniversityofScienceandTechnology),JieWu(HuazhongUniversityofScienceandTechnology),YuanyuanSun(HuazhongUniversityofScienceandTechnology),YunchengGuo(HuazhongUniversityofScienceandTechnology)

Inordertosavehumanlivesandreduceinjuryandpropertyloss,SituationAwareness(SA)informationisessentialandimportantforrescueworkerstoperformtheeffectiveandtimelydisasterrelief.Theinformationisgenerallyderivedfromthesharedimagesviawidelyusedsmartphones.However,conventionalsmartphone-basedimagesharingschemesfailtoefficientlymeettheneedsofSAapplicationsduetotwomainreasons,i.e.,real-timetransmissionrequirementandapplication-levelimageredundancy,whichisexacerbatedbylimitedbandwidthandenergyavailability.Inordertoprovideefficientimagesharingindisasters,weproposeabandwidth-andenergy-efficientimagesharingsystem,calledBEES.ThesalientfeaturebehindBEESistoproposetheconceptofApproximateImageSharing(AIS),whichexploresandexploitsapproximatefeatureextraction,redundancydetection,andimageuploadingtotradetheslightlylowqualityofcomputationresultsincontent-basedredundancyeliminationforhigherbandwidthandenergyefficiency.Nevertheless,theboundariesofthetradeoffsbetweenthequalityofcomputationresultsandefficiencyaregenerallysubjectiveandqualitative.Wehenceproposetheenergy-awareadaptiveschemesinAIStoleveragethephysicalenergyavailabilitytoobjectivelyandquantitativelydeterminethetradeoffsbetweenthequalityofcomputationresultsandefficiency.Moreover,unlikeexistingworkonlyforcross-batchsimilarimages,BEESfurthereliminatesin-batchonesviaasimilarity-awaresubmodularmaximizationmodel.WehaveimplementedtheBEESprototypewhichisevaluatedviathreereal-worldimagedatasets.ExtensiveexperimentalresultsdemonstratetheefficacyandefficiencyofBEES.

Application8:DistributedSystemsandOptimizationsTransparentFault-ToleranceusingIntra-MachineFull-Software-StackReplication GiulianoLosa(VirginiaTech),AntonioBarbalace(VirginiaTech),YuzhongWen(VirginiaTech),MarinaSadini(VirginiaTech),Ho-RenChuang(VirginiaTech),BinoyRavindran(VirginiaTech)

Asthenumberofprocessorsandthesizeofthememoryofcomputingsystemskeepincreasing,thelikelihoodofCPUcorefailures,memoryerrors,andbusfailuresincreasesandcanthreatensystemavailability.SoftwarecomponentscanbehardenedagainstsuchfailuresbyrunningseveralreplicasofacomponentonhardwarereplicasthatfailindependentlyandthatarecoordinatedbyaState-MachineReplicationprotocol.Onecommonsolutionistoreplicatethephysicalmachinetoprovideredundancy,andtorewritethesoftwaretoaddresscoordination.However,aCPUcorefailure,amemoryerror,orabuserrorisunlikelytoalwayscrashanentiremachine.Thus,fullmachinereplicationmaysometimesbeanoverkill,increasingresourcecosts.Inthispaper,weintroducefullsoftwarestackreplicationwithinasinglecommoditymachine.Ourapproachrunsreplicasonfault-independenthardwarepartitions(e.g.,NUMAnodes),whereineachpartitionissoftware-isolatedfromtheothersandhasitsownCPUcores,memory,andfullsoftwarestack.Ahardwarefailureinonepartitioncanberecoveredbyanotherpartitiontakingoveritsfunctionality.WehaverealizedthisvisionbyimplementingFT-Linux,aLinux-basedoperatingsystemthattransparentlyreplicatesrace-free,multithreadedPOSIXapplicationsondifferenthardwarepartitionsofasinglemachine.OurevaluationsofFT-LinuxonseveralpopularLinuxapplicationsshowaworstcaseslowdown(duetoreplication)by~20%.

Apreventiveauto-parallelizationapproachforelasticstreamprocessing RolandKottoKombi(UniversityClaudeBernard),NicolasLumineau(UniversitédeLyon),PhilippeLamarre(INSALyon)

Nowadays,moreandmoresources(connecteddevices,socialnetworks,etc.)emitreal-timedatawithfluctuatingratesovertime.Existingdistributedstreamprocessingengines(SPE)havetoresolveadifficultproblem:deliverresultssatisfyingend-usersintermsofqualityandlatencywithoutover-consumingresources.Thispaperfocusesonparallelizationofoperatorstoadapttheirthroughputtotheirinputrate.Wesuggestanapproachwhichpreventsoperatorcongestioninordertolimitdegradationofresultsquality.Thisapproachreliesonanautomaticanddynamicadaptationofresourceconsumptionforeachcontinuousquery.Thissolutiontakesadvantageofi)ametricestimatingtheactivitylevelofoperatorsinthenearfutureii)theAUTOSCALEapproachwhichevaluatestheneedtomodifyparallelismdegreesatlocalandglobalscopeiii)anintegrationintotheApacheStormsolution.WeshowperformancetestscomparingourapproachtothenativesolutionofthisSPE.

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DependableCloudResourceswithGuardian BaraAbusalah(PurdueUniversity),DerekSchatzlein(PurdueUniversity),JulianJamesStephen(PurdueUniversity),MasoudSaeidaArdekani(PurdueUniversity),PatrickEugster(PurdueUniversity)

Despiteadvancesinmakingdatacentersdependable,failuresstillhappen.Thisisparticularlyonerousforlong-running“bigdata”applications,wherepartialfailurescanleadtosignificantlossesandlengthyrecomputations.BigdataprocessingframeworkslikeHadoopMapReduceincludefaulttolerance(FT)mechanisms,butthesearecommonlytargetedatspecificsystem/failuremodels,andareoftenredundantbetweenframeworks.Thispaperproposestheparadigmofdependableresources:bigdataprocessingframeworksaretypicallybuiltontopofresourcemanagementsystems(RMSs),andproposingFTsupportatthelevelofsuchanRMSyieldsgenericFTmechanisms,whichcanbeprovidedwithlowoverheadbyleveragingconstraintsonresources.WedemonstrateourconceptsthroughGuardian,arobustRMSbasedonYARN.GuardianallowsframeworkstoruntheirapplicationswithindividuallyconfigurableFTgranularityanddegree,withonlyminorchangestotheirimplementation.WedemonstratethebenefitsofourapproachbyevaluatingHadoop,Tez,SparkandPigonGuardianinAmazon-EC2,improvingcompletiontimebyaround68%inthepresenceoffailures,whilemaintainingaround6%overhead.

ACommunication-awareContainerRe-distributionApproachforHighPerformanceVNFs YuchaoZhang(TsinghuaUniversity),YusenLi(NankaiUniversity),KeXu(TsinghuaUniversity),DanWang(HongKongPolytechnicUniversity),MinghuiLi(Baidu),XuanCao(Baidu),QingqingLiang(Baidu)

Containershavebeenusedinmanyapplicationsforisolationpurposesduetothelightweight,scalableandhighlyportableproperties.However,toapplycontainersinvirtualnetworkfunctions(VNFs)facesabigchallengebecausehigh-performanceVNFsoftengeneratefrequentcommunicationworkloadsamongcontainerswhilethecontainercommunicationsaregenerallynotefficient.Comparedwithhardwaremodificationsolutions,properlydistributingcontainersamonghostsisanefficientandlow-costwaytoreducecommunicationoverhead.However,weobservethatthisapproachyieldsatrade-offbetweenthecommunicationoverheadandtheoverallthroughputofthecluster.Inthispaper,wefocusonthecommunication-awarecontainerredistributionproblemtooptimizethecommunicationoverheadandtheoverallthroughputjointlyforVNFclusters.WeproposeasolutioncalledFreeContainerwhichutilizesanoveltwo-stagealgorithmtore-distributecontainersamonghosts.WeimplementFreeContainerinBaiduclusterswith6000serversand35servicesdeployed.Extensiveexperimentsonrealnetworksareconductedtoevaluatetheperformanceoftheproposedapproach.TheresultsshowthatFreeContainercanincreasetheoverallthroughputupto90%withsignificantreductiononcommunicationoverhead.

MinimizingCostinIaaSCloudsviaScheduledInstanceReservation QiushiWang(NanyangTechnologicalUniversity),MingMingTan(NanyangTechnologicalUniversity),XueyanTang(NanyangTechnologicalUniversity),WentongCai(NanyangTechnologicalUniversity)

Regulardiurnalpatternsareoftenseenintheworkloadsofcloud-basedonlineapplications.Thiskindofnon-stationaryworkloadschangestheprocessingdemandsovertime.Torunapplicationserviceswithminimumcosts,thenumberofcloudinstancescanbedynamicallyadjustedaccordingtotheworkloadvariations.Recently,anewtypeofscheduledinstanceshasemergedintheInfrastructure-as-a-Servicemarkettofacilitatesuchconfigurations.Scheduledinstancescanbereservedbasedonarecurringscheduleandtheyofferpricediscounts.Meanwhile,cloudvendorsrequireminimumscheduleddurationstoavoidtheoverheadoffrequentlylaunchingandterminatingcloudinstances.Coupledwithtraditionalon-demandandreservedinstances,itbecomesmorecomplicatedforuserstofindtheoptimalcombinationofthesethreepricingoptionstominimizetheirmonetarycosts.Forthenewscheduledinstances,notonlythenumberofinstancesbutalsotheirstartandstoptimeshavetobedecided.Inthispaper,wedevelopafastandeffectivestrategytosolvethisproblem.Basedonthehourlyworkloaddistributions,wefirstcomputetheoptimalnumberofinstancestoacquireforeachpricingoption.Then,wedesignaschedulingalgorithmtoarrangethescheduledinstancesincompliancewiththerestrictionoftheirscheduleddurations.UsingtheworkloadsoftheLOLonlinegameandtheWikipediaMobileserviceastwocasestudies,theefficacyofourstrategyisdemonstrated.

EfficientDistributedCoordinationatWAN-scale AilidaniAilijiang(SUNYBuffalo),AlekseyCharapko(SUNYBuffalo),MuratDemirbas(SUNYBuffalo),BekirOguzTurkkan(SUNYBuffalo),TevfikKosar(SUNYBuffalo)

Traditionalcoordinationservicesfordistributedapplicationsdonotscalewelloverwide-areanetworks(WAN):centralizedcoordinationfailstoscalewithrespecttotheincreasingdistancesintheWAN,anddistributedcoordinationfailstoscalewithrespecttothenumberofnodesinvolved.WearguethatitispossibletoachievescalabilityoverWANusingahierarchicalcoordinationarchitectureandasmarttokenmigrationmechanism,andlaydownthefoundationofanoveldesignforaflexible-consistentcoordinationframework,calledWanKeeper.WeimplementedWanKeeperbasedontheZooKeeperAPIanddeployeditoverWANasaproofofconcept.OurexperimentalresultsbasedontheYahoo!CloudServingBenchmark(YCSB),ApacheBookKeeperreplicatedlogservice,andtheSharedCloud-backedFileSystem(SCFS)showthatWanKeeperprovidesmultiplefoldsimprovementinwrite/updateperformanceinWANcomparedtoZooKeeper,whilekeepingthesamereadperformance.

Application9:DistributedSystemsandApplicationsSpecifyingaDistributedSnapshotAlgorithmasaMeta-programandModelCheckingitatMeta-level HaThiThuDoan(JapanAdvancedInstituteofScienceandTechnology),FrancoisBonnet(OsakaUniversity),KazuhiroOgata(JapanAdvancedInstituteofScienceandTechnology)

ThepaperproposesanewapproachtomodelcheckingChandy-LamportDistributedSnapshotAlgorithm(CLDSA).TheessentialoftheapproachisthatCLDSAisspecifiedasameta-programinMaudesuchthatthemeta-programtakesaspecificationofanunderlyingdistributedsystem(UDS)andgeneratesthespecificationoftheUDSonwhichCLDSAissuperimposed(UDS-CLDSA).TomodelcheckthataUDS-CLDSAenjoysadesiredproperty,itsufficesthathumanusersspecifytheUDSfortheproposedapproach,whilehumanusersneedtospecifytheUDS-CLDSAfortheexistingapproachforeachUDS.Sincetheproposedapproachconductsmodelcheckingatmeta-level,itproducesacounterexampleifaUDS-CLDSAdoesnotenjoytheproperty,whiletheexistingapproachdoesnot.OurmethodspecifyingCLDSAasameta-programcanbeappliedtoformalspecificationoftheclassofdistributedalgorithmsthataresuperimposedonUDSs.

Self-EvolvingSubscriptionsforContent-BasedPublish/SubscribeSystems

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CesarCañas(McGillUniversity),KaiwenZhang(TechnicalUniversityofMunich),BettinaKemme(McGillUniversity),JörgKienzle(McGillUniversity),Hans-ArnoJacobsen(TechnicalUniversityofMunich)

Traditionalpub/subsystemscannotadequatelyhandleworkloadsofapplicationswithdynamic,short-livedsubscriptionssuchaslocation-basedsocialnetworks,predictivestocktrading,andonlinegames.Subscribersmustcontinuouslyinteractwiththepub/subsystemtoremoveandinsertsubscriptions,therebyinefficientlyconsumingnetworkandcomputingresources,andsacrificingconsistency.Intheaforementionedapplications,werecognizethatthechangesinthesubscriptionscanfollowapredictablepatternoversomevariable(e.g.,time).Inthispaper,wepresentanewtypeofsubscription,calledevolvingsubscription,whichencapsulatesthesepatternsandallowthepub/subsystemtoautonomouslyadapttothedynamicinterestsofthesubscriberswithoutincurringanexpensivere-subscriptionoverhead.Weproposeageneralmodelforexpressingevolvingsubscriptionsandaframeworkforsupportingtheminapub/subsystem.Tothisend,weproposethreedifferentdesignstosupportevolvingsubscriptions,whichareevaluatedandcomparedtothetraditionalresubscriptionapproachinthecontextoftwousecases:onlinegamesandhigh-frequencytrading.Ourevaluationshowsthatoursolutionscanreducesubscriptiontrafficby96.8%andimprovedeliveryaccuracywhencomparedtothebaselineresubscriptionmechanism.

ScalableRoutingforTopic-basedPublish/SubscribeSystemsunderFluctuations VolkerTurau(HamburgUniversityofTechnology),GerrySiegemund(HamburgUniversityofTechnology)

Theloosecouplingandtheinherentscalabilitymakepublish/subscribesystemsanidealcandidateforevent-drivenservicesforwirelessnetworksusinglowpowerprotocolssuchasIEEE802.15.4.Thisworkintroducesadistributedalgorithmtobuildandmaintainaroutingstructureforsuchnetworks.Thealgorithmdynamicallymaintainsamulticasttreeforeachnode.Whilepreviousworkfocusedonminimizingthesetreesweaimtokeeptheefforttomaintainthemincaseoffluctuationsofsubscriberslow.Themulticasttreesareimplicitlydefinedbyanovelstructurecalledaugmentedvirtualring.Themaincontributionisadistributedalgorithmtobuildandmaintainthisaugmentedvirtualring.Maintenanceoperationsaftersub-andunsubscriptionsrequiremessageexchangeinalimitedregiononly.Wecomparetheaveragelengthsoftheconstructedforwardingpathswithanalmostidealapproach.AsaresultofindependentinterestwepresentadistributedalgorithmusingmessagesofsizeO(logn)forconstructingvirtualringsofgraphsthatareonaverageshorterthanringsbasedondepthfirstsearch.

OPPay:DesignandImplementationofAPaymentSystemforOpportunisticDataServices FengruiShi(ImperialCollegeLondon),ZhijinQin(ImperialCollegeLondon),JulieMcCann(ImperialCollegeLondon)

Thelargenumberofpersonalwirelessdevicesintheurbanareascouldbeusedtoprovidevariousopportunisticdataservices,suchasWiFisharing,content-basedfilesharingandopportunisticnetworking.Inordertofacilitatetheseservices,itisessentialtoincentivisethedeviceownerstobecomeserviceproviders.However,previousresearchfailedtodeliveranypracticalpaymentsystemsforopportunisticdataservices.Inspiredbysmartcontractsfunctionalitiesofbitcoin,thispaperproposesapaymentsystemnamedOPPayforopportunisticdataservices,whichimplementsamicropaymentcommunicationprotocolformobiledevicestoperformdatatransactionsandmakepaymentsusingbitcoin.Thesystemisdesignedtomakeincrementalpaymentsandthusresilienttointerruptedcommunicationscausedbyhumanmobilityinthemobilenetwork.Byimplementingandevaluatingthesystemforthreedifferentapplications,weshowthatthesystemisabletoworkinheterogeneoushardwareandsoftwareenvironmentsandcanachievefasttransactionsconfirmationwithsmallfeeoverheadandlowfaultypaymentvalue.

OptimalResourceAllocationforMulti-userVideoStreamingovermmWaveNetworksZhifengHe(AuburnUniversity),ShiwenMao(AuburnUniversity)

Weinvestigatetheresourceallocationproblem,includingtimeslotallocation,channelallocation,andpoweradaptation,inamillimeterWave(mmWave)networkwithmultipletransmissionlinks,multiplechannels,andaPicoNetCoordinator(PNC).Eachlinkhasavideosessiontotransmitfromthetransmittertothereceiver.Theobjectiveistominimizethenumberoftimeslotstofinishthevideosessionsofalllinksbyjointlyoptimizingchannelallocationandtimeslotallocationforlinks,whileconsideringthepossibleinterferencebetweendifferentlinksonthesamechannel.Theoptimalsolutionfortheformulatedproblemiscomputationallyprohibitivetoobtainduetotheexponentialcomplexity.Wedevelopedacolumngenerationbasedmethodtoreformulatetheoriginalproblemintoamainproblemalongwithaseriesofsub-problems,withgreatlyreducedcomplexity.Weprovethattheoptimalsolutionforthereformulatedproblemconvergestotheoptimalsolutionoftheoriginalproblem,andwederivedalowerboundfortheperformanceofthereformulatedproblemateachiteration,whichwillfinallyconvergetotheglobaloptimalsolution.Theproposedschemeisvalidatedwithsimulationswithitssuperiorperformanceoverexistingworkisobserved.

AMulti-AgentParallelApporachtoAnalyzingLargeClimateDataSets JasonWoodring(UniversityofWashingtonBothell),MatthewSell(UniversityofWashingtonBothell),MunehiroFukuda(UniversityofWashingtonBothell),HazelineAsuncion(UniversityofWashingtonBothell),EricSalathe(UniversityofWashingtonBothell)

Despitevariouscloudtechnologiesthathaveparallelizedandscaledupbigdataanalysis,theytargetdatamostlyintextswhichareeasytopartitionandthuseasytomapoveraclustersystem.Therefore,theirparallelizationdonotnecessarilycoverscientificstructureddatasuchasNetCDForneedadditional,user-providedtoolstoconverttheoriginaldataintospecificformats.Tofacilitateuser-intuitiveparallelizationofsuchscientificdataanalysis,thispaperpresentsanagent-basedapproachthatinstantiatesdistributedarraysoveraclustersystem,maintainsstructuredscientificdatainthesearrays,deploysmanymobileagentsoverthearraystoperformcomputationalactionsondata,andcollectsnecessaryresults.Todemonstratethepracticabilityofouragent-basedapproach,wefocusedonclimatechangeresearchandimplementedaweb-interfacedclimateanalysis,usingtheMASS(multi-agentspatialsimulation)library.Inthispaper,weshowpracticaladvantagesof,performanceimprovementsby,andchallengesforouragent-basedapproachinstructureddataanalysis.

Application10:DistributedSystemsandServicesEnergyProportionalServers:WhereAreWein2016? CongfengJiang(HangzhouDianziUniversity),YumeiWang(HangzhouDianziUniversity),DongyangOu(HangzhouDianziUniversity),BingLuo(WayneStateUniversity),WeisongShi(WayneStateUniversity)

Thehugeenergyconsumptionindatacentersproducesnotonlyhighelectricitybillbutalsotremendouscarbonfootprints.Althoughtoday’sserversanddatacentersofleadinginternetcompaniesaremoreenergyefficientthaneverbefore,thefluctuationsinexternalworkloadandinternalresourceutilizationcallsforenergyproportionalcomputing.Insightintoserverenergyproportionalitycanhelpimproveworkloadplacementwhilealsoreducingenergyconsumption.Inthispaper,weinvestigateall477validpublishedresultsofSPECpower_ssjbenchmarkfrom2007to2016Q3andreorganizethembyhardwareavailabilityyearformoreaccurateanalysisonproductionservers.Throughcomprehensiveanalysiswefindthat:(1)Thespeciousstagnationofenergyproportionalityinrecentyearsis

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mainlycausedbytheadoptionofprocessorsofspecificmicroarchitectureandisnottheindicativetrendofenergyproportionalityimprovement.(2)Microarchitectureevolutionhasmoreinfluenceonenergyefficiencyimprovementthanenergyproportionality.(3)Today’sservers’peakenergyefficienciesareshiftingfrom100%resourceutilizationto80%or70%utilizationandserverenergyproportionalityimproveswithsuchshifting.Wethenconductextensiveexperimentson4rackserverstoinvestigatetheenergyefficiencyvariationsunderdifferenthardwareconfigurations,includingmemorypercoreinstallationandprocessorfrequencyscaling.Ourexperimentsshowthathardwareconfigurationhassignificantimpactonserver’senergyefficiency.Ourfindingspresentedinthispaperprovideusefulinsightsandguidancetosystemdesigners,aswellasdatacenteroperatorsforenergyproportionalityawareworkloadplacementandenergysavings.

AreHTTP/2ServersReadyYet? MuhuiJiang(TheHongKongPolytechnicUniversity),XiapuLuo(TheHongKongPolytechnicUniversity),TungngaiMiu(NexusguardLimited),ShengtuoHu(TheHongKongPolytechnicUniversity),WeixiongRao(TongjiUniversity)

SupersedingHTTP/1.1,thedominatingwebprotocol,HTTP/2promisestomakewebapplicationsfasterandsaferbyintroducingmanynewfeatures,suchasmultiplexing,headercompression,requestpriority,serverpush,etc.AlthoughafewrecentstudiesexaminedtheadoptionofHTTP/2andevaluateditsimpacts,littleisknownaboutwhetherthepopularHTTP/2servershavecorrectlyrealizedthenewfeaturesandhowthedeployedserversusethesefeatures.Tofillinthegap,inthispaper,weconductthefirstsystematicinvestigationbyinspectingsixpopularimplementationsofHTTP/2servers(i.e.,Nginx,Apache,H2O,Lightspeed,nghttpdandTengine)andmeasuringthetop1millionAlexawebsites.Inparticular,weproposenewmethodsanddevelopatoolnamedH2Scopetoassessthenewfeaturesinthoseservers.Theresultsofthelarge-scalemeasurementonHTTP/2websitesrevealnewobservationsandinsights.ThisstudyshedslightonthecurrentstatusandthefutureresearchofHTTP/2.

DataIntegrityforCollaborativeApplicationsoverHostedServices ErtemEsiner(NanyangTechnologicalUniversity),AnwitamanDatta(NanyangTechnologicalUniversity)

Inthisworkwefocusonintegrityandconsistencyofdataaccessedandmanipulatedbymultiplecollaboratingusers,andstoredinan(untrusted)hostedservice.Thisisaproblem,aspectsofwhichhavebeenstudiedinisolationinhithertodistinctcommunities.Consistencyisoneofthecardinalproblemsofdistributedcomputing.Integrityofhosteddatahasbeenstudiedoverthelastdecade,andnumeroustechniquesforproofofdatapossessionand/orretrievabilityhavebeenexplored.Thelatterlineofworkhoweverhaveoftenassumedstaticdata,andtechniquestohandledynamicorversioneddatahaveonlyveryrecentlybeenproposed.Yet,eventheexistingsolutionsthathandlemutablecontentdosoundertheassumptionthatonlyasingledataowner(usingasingleclient)manipulateandverifysaiddata.Thisisaseriouslimitationintermsofthevarietyofapplicationsthatcanbenefitfromsuchmechanismsforproofofdatapossession.Thenovelty,andprimarycontributionofthisworkisinfillingthisgap.Specifically,weextendtheexistingideasofproofofpossessionofdynamicdata,inordertosupportmultipleuserswhomaycollaborateinrealtimeorasynchronously.Incontrast(andaddition)tothechallengeofanuntrustedstorageserverthatexistingtechniquesforproofofdatapossessionneedtoovercome,wehadto,simultaneouslyaccountfordataintegrityviolationsthatmaybeincurredduetoalltheusualchallengesofmaintainingconsistencyofcollaborativedata(evenifthestorageserverwastrusted).

VirtualMachinePowerAccountingwithShapleyValue WeixiangJiang(HuazhongUniversityofScience&Technology),FangmingLiu(HuazhongUniversityofScienceandTechnology),GuomingTang(UniversityofVictoria),KuiWu(UniversityofVictoria),HaiJin(HuazhongUniversityofScience&Technology)

Theever-increasingpowerconsumptionofdatacentershaseatenupalargeportionoftheirprofit.Onepossiblesolutionistochargedatacenterusersfortheiractualpowerusage.However,itposesagreattechnicalchallengeasthepowerofVMsco-existinginaphysicalmachinecannotbemeasureddirectly.ItisthuscriticaltodevelopafairmethodtodisaggregatethepowerofaphysicalmachinetoindividualVMs.Wetackletheabovechallengebymodelingthepowerdisaggregationproblemasacooperativegameandproposenon-deterministicShapleyvaluetodiscoverthefairpowershareofVMs(inthesenseofsatisfyingfourdesiredaxiomaticprinciples),whilecompensatingthenegativeimpactofVMpowervariation.Wedemonstratethattheresultsfromexistingpowermodel-basedsolutioncandeviatefromthe“groundtruth”by25.22%-46.15%.AndcomparedwiththeexactShapleyvalue,ournon-deterministicShapleyvaluecanachievelessthan5%errorfor90%ofthetime.

AVersatilePlatformforMobileDataGatheringExperimentsinWirelessSensorNetworks JiLi(StonyBrookUniversity),CongWang(StonyBrookUniversity),YuanyuanYang(StonyBrookUniversity)

Inrecentyears,mobiledatagatheringinwirelesssensornetworkshasattractedmuchinterestsintheresearchcommunity.However,despiteextensiveefforts,manyofpreviousworkinthisarealiesonlyintheoryandevaluatesnetworkperformancewithcomputersimulations,whichleavesalargegapfromreality.Inthispaper,wepresentthedesignandimplementationofageneralpurpose,flexibleplatformformobiledatagatheringinwirelesssensornetworkstoevaluatenetworkperformanceandalgorithmsinapracticalsetting.Insteadofrelyingonhand-craftedtheoreticalmodels,ourplatformintegratesbothmobiledatacollectorandsensornodestoproviderealisticperformanceevaluations.Inaddition,theplatformadoptsamodulardesigninmobiledatacollectorandsensornodes,andequipsthemobiledatacollectorwithadvancedcomputingcapability,whichmakesitversatileforevaluatingtheperformanceofawide-rangeofapplications.Finally,asacasestudy,weimplementawildlifemonitoringsystemonourplatform.Ourexperimentalresultsdemonstratethatrealimplementationscanevaluatemanypracticalperformancefactorswhichwouldhaveagreatimpactonthesensingresultsandareverydifficulttofullycapturebytheoreticalmodelsandsimulations.Weexpectthatthisplatformcanbecomeaverypowerfulgeneraltoolformoreaccuratenetworksimulationsandfacilitateperformanceoptimizationinwirelesssensornetworks.

OnDirectionalNeighborDiscoveryinmmWaveNetworks YuWang(AuburnUniversity),ShiwenMao(AuburnUniversity),TheodoreS.Rappaport(NewYorkUniversity)

Thedirectionalneighbordiscoveryproblem,i.e.,spatialrendezvous,isafundamentalprobleminmillimeterwave(mmWave)networks.Thechallengeishowtoletthetransmitterandreceiverbeamsmeetinspaceunderdeafnesscausedbydirectionaltransmissionandreception.Inthispaper,wepresentaHunting-basedDirectionalNeighborDiscovery(HDND)scheme,whereanodecontinuouslyrotatesitsdirectionalbeamtoscanitsneighborhoodforneighbors.Througharigorousanalysis,wederivetheconditionsforensuredneighbordiscovery,aswellasaboundfortheworstcasediscoverytime.Wevalidatetheanalysiswithextensivesimulations,anddemonstratethesuperiorperformanceoftheproposedschemeovertwobenchmarkschemes.

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Vision/Blue Sky Thinking Track Paper Abstracts

Vision1:InternetofThings,SmartCitiesandCyber-PhysicalSystemsObservable-by-Design MasaruKitsuregawa(NationalInstituteofInformatics(NII)/InstituteofIndustrialScience,UniversityofTokyo)

Wepresenttheobservable-by-designprinciple.Webelieve,thatthenewgenerationofservices,products,andenvironment,managementsystemsshouldbedesignedtoadapttochanges.,Therefore,theyshouldbedesignedtobeobservable,andtheirdesign,shouldproactivelyandreactivelyadapttothechangesobserved,bothinternallyandexternally.Twoconcreteexamplesillustrate,theapplicationofobservable-by-designprinciple:(1)ship,buildingandmanagement,and(2)riverdamwaterflowmanagement.,Webelievethattheobservable-by-designprinciplecanbe,appliedinalargescale.Inthelongterm,anewgenerationofobservable-by-design,infrastructurescanbebuiltthatincorporates,thesensingandadaptingcapabilitiesintheirconstruction.

AnArchitecturalVisionforaData-CentricIoT:RethinkingThings,TrustandClouds EveM.Schooler(Intel),DavidZage(Intel),JeffSedayao(Intel),HassnaaMoustafa(Intel),AndrewBrown(Intel),MorenoAmbrosin(UniversityofPadua)

TheInternetofThings(IoT)isproducingatidalwaveofdata,muchofitoriginatingatthenetworkedgeandoriginatingfromapplicationswithrequirementsunmetbythetraditionalback-endCloudarchitecture.Toaddressthedisruptioncausedbytheoceanofdata,thispaperoffersaholisticdata-centricarchitecturalvisionforthedata-centricIoT.Itadvocatesthatwerethinkourapproachtothedesignanddefinitionofkeyelements:thatweshiftourfocusfromThingstoSmartObjects;growTrustorganically;andevolveback-endCloudstowardEdgeandFogclouds,whichleveragedata-centricnetworksandenableoptimalhandlingofupstreamdataflows.Alongtheway,wewaxpoeticaboutseveralblue-skytopics,assessthestatusoftheseelementsinthecontextofrelatedwork,andidentifyknowngapsinmeetingthisvision.

EdgeComputingandIoTBasedResearchforBuildingSafeSmartCitiesResistanttoDisasters TeruoHigashino(OsakaUniversity),HirozumiYamaguchi(OsakaUniversity),AkihitoHiromori(OsakaUniversity),AkiraUchiyama(OsakaUniversity),KeiichiYasumoto(NaraInstituteofScienceandTechnology)

Recently,severalresearchesconcerningwithsmartandconnectedcommunitieshavebeenstudied.Soonthe4G/5Gtechnologybecomespopular,andcellularbasestationswillbedenselylocatedintheurbanspace.Theymayofferintelligentservicesforautonomousdriving,urbanenvironmentimprovement,disastermitigation,elderly/disabledpeoplesupportandsoon.Suchinfrastructuremightfunctionasedgeserversfordisastersupportbase.Inthispaper,weenumerateseveralresearchissuestobedevelopedintheICDCScommunityinthenextdecadeinorderforbuildingsafesmartcitiesresistanttodisasters.Inparticular,wefocuson(A)up-to-dateurbancrowdmobilitypredictionand(B)resilientdisasterinformationgatheringmechanismsbasedontheedgecomputingparadigm.Weinvestigaterecentrelatedworksandprojects,andintroduceouron-goingresearchworkandinsightfordisastermitigation.

TheInternetofThingsandMultiagentSystems:DecentralizedIntelligenceinDistributedComputing MunindarSingh(NorthCarolinaStateUniversity),AmitChopra(LancasterUniversity)

Traditionally,distributedcomputingconcentratesoncomputationunderstoodatthelevelofinformationexchangeandsetsasidehumanandorganizationalconcernsaslargelytobehandledinanadhocmanner.Increasingly,however,distributedapplicationsinvolvemultiplelociofautonomy.Researchinmultiagentsystems(MAS)addressesautonomybydrawingonconceptsandtechniquesfromartificialintelligence.However,MASresearchgenerallylacksanadequateunderstandingofmoderndistributedcomputing.InthisBlueSkypaper,weenvisiondecentralizedmultiagentsystemsasawaytoplacedecentralizedintelligenceindistributedcomputing,specifically,bysupportingcomputationatthelevelofsocialmeanings.WemotivateourproposalsforresearchinthecontextoftheInternetofThings(IoT),whichhasbecomeamajorthrustindistributedcomputing.FromtheIoTsrepresentativeapplications,weabstractoutthemajorchallengesofrelevancetodecentralizedintelligence.TheseincludetheheterogeneityofIoTcomponents;asynchronousanddelay-tolerantcommunicationanddecoupledenactment;andmultiplestakeholderswithsubtlerequirementsforgovernance,incorporatingresourceusage,cooperation,andprivacy.TheIoTyieldshigh-impactproblemsthatrequiresolutionsthatgobeyondtraditionalwaysofthinking.Weconcludewithhighlightsofsomepossibleresearchdirectionsindecentralizedmultiagentsystems,includingprogrammingmodels;interaction-orientedsoftwareengineering;andwhatwetermenlightenedgovernance.

InternetofThings:FromSmall-toLarge-ScaleOrchestration CharlesConsel(Inria/BordeauxINP),MilanKabac(ImperialCollege)

ThedomainofInternetofThings(IoT)israpidly,expandingbeyondresearch,andbecomingamajorindustrial,marketwithsuchstakeholdersasmajormanufacturersofchips,andconnectedentities(i.e.,things),andfast-growingoperators,ofwide-areanetworks.Importantly,thisemergingdomainis,drivenbyapplicationsthatleverageanIoTinfrastructureto,provideuserswithinnovative,high-valueservices.IoTinfrastructures,rangefromsmallscale(e.g.,homesandpersonal,health)tolargescale(e.g.,citiesandtransportationsystems).Inthispaper,wearguethatthereisacontinuumbetweenorchestrating,connectedentitiesinthesmallandinthelarge.We,proposeaunifiedapproachtoapplicationdevelopment,which,coversthisspectrum.Todoso,weexaminetherequirementsfor,orchestratingconnectedentitiesandaddressthemwithdomainspecific,designconcepts.Wethenshowhowtomapthesedesign,conceptsintodedicatedprogrammingpatternsandruntime,mechanisms.Ourworkrevolvesarounddomain-specificconceptsand,notations,integratedintoatool-baseddesignmethodologyand,dedicatedtodevelopIoTapplications.Wehaveappliedour,workacrossaspectrumofinfrastructuresizes,rangingfrom,anautomatedpilotinavionics,toanassistedlivingplatform,forthehomeofseniors,toaparkingmanagementsystemin,asmartcity.

EdgeOS_H:AHomeOperatingSystemforInternetofEverything JieCao(WayneStateUniversity),LanyuXu(WayneStateUniversity),RaefAbdallah(WayneStateUniversity),WeisongShi(WayneStateUniversity)

TheproliferationofInternetofEverything(IoE)andthesuccessofrichCloudserviceshavepushedthehorizonofanewcomputingparadigm,EdgeComputing,whichcallsforprocessingthedataattheedgeofthenetwork.SmarthomeasatypicalIoEapplicationisbeingwidelyadaptedintopeopleslife.EdgeComputinghasthepotentialtoempowerthesmarthome,butitneedsmorecontributionfromthecommunitybeforeittrulybenefitsourlives.Inthispaper,wefirstpresentthe

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visionofEdgeOSH,ahomeoperatingsystemforInternetofEverything,followedbythechallengesinEdgeOSH,namelyprogramminginterface,self-management,datamanagement,security&privacy,andnaming.Withineachchallengewealsodiscussthepotentialdirectionsthatareworthfurtherinvestigation.

Vision2:FutureNetworkingandCyberinfrastructureAVisionforZero-HopNetworking(ZeN) MostafaAmmar(SchoolofComputerScience,GeorgiaTech),EllenZegura(SchoolofComputerScience,GeorgiaTech),YimengZhao(SchoolofComputerScience,GeorgiaTech)

Ithasbecomeincreasinglyimportantforcontentproviders(CPs)toreachconsumerswithlowlatency.PeeringlinksthatconnectCPsdirectlytoaccessInternetserviceproviders(accessISPs)havebeenusedforthispurposethusprovidingone-hopASpathsfromCPstousers.Whileprovidingimprovedlatency,thesepeeringlinksstilldonotgiveCPscontrolovertheentireend-to-endpathtotheirusers.ThishasmadeitdifficultforCPstocompletelymanageuserexperience.Motivatedbythis,weproposethedeploymentofZero-HopNetworks(ZeN),whereaCP'sentireend-to-endpathtousersisunderitscontrol.WebelieveitisimportanttorespondtothecompellingdemandforZeNandenableitsprovisionoverthesharedInternetinfrastructuresothatallmaycontinuetoreapitsbenefits.InthispaperwelayoutthevisionforZeN,describingitsgoalsandchallenges.WeproposetodeployZeNbyallowingCPstoextendtheirnetwork'scontrolovertheaccessISPsubstrateinawaythatallowstheCPtocontroltheentireend-to-endpath.WedeveloptwostrawmanarchitecturesbasedonSoftware-DefinedNetworkingideas:onebasedonresourcereservationandtheotherbasedonnetworkvirtualization.WealsodiscusssomeelementsofaresearchagendathatisneededtobringZeNdeploymentstorealization.

StructuredOverlayNetworksforaNewGenerationofInternetServices AmyBabay(JohnsHopkinsUniversity),ClaudiuDanilov(BoeingResearchandTechnology),JohnLane(LTNGLobalCommunications),MichalMiskin-Amir(LTNGlobalCommunications,SpreadConceptsLLC),DanielObenshain(JohnsHopkinsUniversity),JohnSchultz(LTNGlobalCommunications,SpreadConceptsLLC),JonathanStanton(LTNGlobalCommunications,SpreadConceptsLLC),ThomasTantillo(JohnsHopkinsUniversity),YairAmir(JohnsHopkinsUniversity,LTNGlobalCommunications,SpreadConceptsLLC)

ThedramaticsuccessandscalingoftheInternetwasmadepossiblebythecoreprincipleofkeepingitsimpleinthemiddleandsmartattheedge(ortheend-to-endprinciple).However,newapplicationsbringnewdemands,andformanyemergingapplications,theInternetparadigmpresentslimitations.ForapplicationsinthisnewgenerationofInternetservices,structuredoverlaynetworksofferapowerfulframeworkfordeployingspecializedprotocolsthatcanprovidenewcapabilitiesbeyondwhattheInternetnativelysupportsbyleveragingglobalstateandin-networkprocessing.Thestructuredoverlayconceptincludesthreeprinciples:Aresilientnetworkarchitecture,aflexibleoverlaynodesoftwarearchitecturethatexploitsglobalstateandunlimitedprogrammability,andflow-basedprocessing.Wedemonstratetheeffectivenessofstructuredoverlaynetworksinsupportingtodaysdemandingapplicationsandproposeforward-lookingideasforleveragingtheframeworktodevelopprotocolsthatpushtheboundariesofwhatispossibleintermsofperformanceandresilience.

EnsuringNetworkNeutralityforFutureDistributedSystems ThiagoGarrett(FederalUniversityofParana),SchahramDustdar(TUWien),LuisC.E.Bona(FederalUniversityofParana),EliasP.DuarteJr.(FederalUniversityofParana)

NetworkNeutralityisessentialforensuringalevelplayingfieldforthedevelopmentofnewapplicationsandservicesontheInternet.Lawsandrulesalonemightnotbeenoughtoprotectinnovation,faircompetitionandconsumersfreedomofchoiceonline.TheresearchcommunityhastheresponsibilitytoproposesolutionsthatrevealdiscriminatorytrafficmanagementmechanismsontheInternet.Wepresentthepotentialrisksofanon-neutralInternet,identifyseveralopenchallengesfordesigningsolutionsthatdetecttrafficdifferentiation,andproposeamodelthataddressessuchchallengesbytakingadvantageofdistributedsystemstechnologies.

UncoveringtheUsefulStructuresofComplexNetworksinSocially-RichandDynamicEnvironments JieWu(TempleUniversity)

Manygroupactivitiescanberepresentedasacomplexnetworkwhereentities(vertices)areconnectedinpairsbylines(edges).Uncoveringausefulglobalstructureofcomplexnetworksisimportantforunderstandingsystembehaviorsandinprovidingglobalguidanceforapplicationdesigns.Webrieflyreviewexistingnetworkmodels,discussseveraltoolsusedinthetraditionalgraphtheory,distributedcomputing,distributedsystems,andsocialnetworkcommunities,andpointouttheirlimitations.Wediscussopportunitiestouncoverthestructuralpropertiesofcomplexnetworks,especiallyinamobileenvironment,andwesummarizethreepromisingapproachesforuncoveringusefulstructures:trimming,layering,andremapping.Finally,wepresentsomechallengesinalgorithmictechniques,withafocusondistributedandlocalizedsolutions,torepresentvariousstructures.

FutureNetworkingChallenges:TheCaseofMobileAugmentedReality TristanBraud(TheHongKongUniversityofScienceandTechnology),FarshidHassaniBijarbooneh(TheHongKongUniversityofScienceandTechnology),DimitrisChatzopoulos(TheHongKongUniversityofScienceandTechnology),PanHui(TheHongKongUniversityofScienceandTechnology)

Mobileaugmentedreality(MAR)applicationsaregainingpopularityduetothewideadoptionofmobileandespeciallywearabledevices.SuchdevicesoftenpresentlimitedhardwarecapabilitieswhileMARapplicationsoftenrelyoncomputationallyintensivecomputervisionalgorithmswithextremelatencyrequirements.Tocompensateforthelackofcomputingpower,offloadingdataprocessingtoadistantmachineisoftendesired.However,thisprocessintroducesnewconstrainsintheapplication,especiallyintermsoflatencyandbandwidth.Ifcurrentnetworkinfrastructuresarenotreadyforsuchtraffic,weenvisionthatfuturewirelessnetworkssuchas5GwillrapidlybesaturatedbyresourcehungryMARapplications.Moreover,duetothehighvarianceofwirelessnetworks,MARapplicationsshouldnotrelyonlyontheevolutionofinfrastructures.Inthisarticle,weanalyzeMARapplicationsandjustifytheirneedforaccessingexternalinfrastructure.Afterareviewoftheexistingnetworkinfrastructuresandprotocols,wedefineguidelinesforfuturereal-timeandmultimediatransportprotocols,withafocusonMARoffloading.

SoftwareDefinedCyberinfrastructure IanFoster(ArgonneNationalLaboratoryandTheUniversityofChicago),BenBlaiszik(TheUniversityofChicago),KyleChard(ComputationInstitute,UniversityofChicagoandArgonneNationalLab),RyanChard(VictoriaUniversityofWellington)

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Withinandacrossthousandsofsciencelabs,researchersandstudentsstruggletomanagedataproducedinexperiments,simulations,andanalyses.Largelymanualresearchdatalifecyclemanagementprocessesmeanthatmuchtimeiswasted,researchresultsareoftenirreproducible,anddatasharingandreuseremainrare.Inresponse,weproposeanewapproachtodatalifecyclemanagementinwhichresearchersareempoweredtodefinetheactionstobeperformedatindividualstoragesystemswhendataarecreatedormodified:actionssuchasanalysis,transformation,copying,andpublication.Wetermthisapproachsoftware-definedcyberinfrastructurebecauseuserscanimplementpowerfuldatamanagementpoliciesbydeployingrulestolocalstoragesystems,muchassoftware-definednetworkingallowsuserstoconfigurenetworksbydeployingrulestoswitches.Wearguethatthisapproachcanenableanewclassofresponsivedistributedstorageinfrastructurethatwillaccelerateresearchinnovationbyallowinganyresearchertoassociatedataworkflowswithdatasources,whetherlocalorremote,forsuchpurposesasdataingest,characterization,indexing,andsharing.Wereportonearlyexperimentswiththisapproachinthecontextofexperimentalscience,inwhichasimpleif-trigger-then-action(IFTA)notationisusedtodefinerules.

Vision3:NextGenerationCloudandEdgeServicesComputingintheContinuum:CombiningPervasiveDevicesandServicestoSupportData-drivenApplications ManishParashar(RutgersUniversity),MoustafaAbdelbaky(RutgersUniversity),MengsongZou(RutgersUniversity),AliRezaZamani(RutgersUniversity),EduardRenart(RutgersUniversity),JavierDiaz-Montes(RutgersUniversity)

Theexponentialgrowthofdigitaldatasourceshas,thepotentialtotransformallaspectsofsocietyandourlives.,However,toachievethisimpactthedatahastobeprocessed,inatimelymannertoextractcriticalinsightsthatcandrive,decisionmaking.Further,traditionalapproachesthatrelyon,movingdatatoremotedatacentersforprocessingarenolonger,feasible.Instead,newapproachesthateffectivelyleveragedistributed,computationalinfrastructureandservicesarenecessary.,Specifically,theseapproachesmustseamlesslycombineresources,andservicesattheedge,inthecore,andalongthedata,pathasneeded.Thispaperpresentsourvisionforenabling,anapproachforcomputinginthecontinuum,i.e.,realizinga,fluidecosystemwheredistributedresourcesandservicesare,programmaticallyaggregatedon-demandtosupportemerging,data-drivenapplicationworkflows.Thisvisioncallsfornovel,solutionsforfederatinginfrastructure,programmingapplications,andservices,andcomposingdynamicworkflows,whichare,capableofreactinginreal-timetounpredictabledatasizes,,availabilities,locations,andrates.

Decision-drivenExecution:ADistributedResourceManagementParadigmfortheAgeofIoT TarekAbdelzaher(UIUC),TanvirAlAmin(UIUC),AmotzBar-Noy(UIUC),WilliamDron(BBN),RameshGovindan(USC),ReginaldHobbs(ARL),ShaohanHu(IBM),Jung-EunKim(UIUC),ShuochaoYao(UIUC),YiranZhao(UIUC)

Thispaperintroducesanovelparadigmforresourcemanagementindistributedsystems,calleddecision-drivenexecution.Theparadigmisappropriateformission-drivensystems,wherethegoalistoenablefaster,leaner,andmoreeffectivedecisionmaking.Allresourceconsumption,inthisparadigm,istiedtotheneedsofmakingdecisionsonalternativecoursesofaction.Apointofdeparturefromtraditionalarchitecturesliesininterfacesthatallowapplicationstospecifytheirunderlyingdecisionlogic.Thisspecification,inturn,allowsthesystemtoreasonaboutmosteffectivemeanstomeetinformationneedsofdecisions,resultinginsimulataneousoptimizationofdecisionaccuracy,cost,andspeed.Thepaperdiscussestheoverallvisionofdecision-drivenexecution,outliningpreliminaryworkandnovelchallenges.

ACTiCLOUD:EnablingtheNextGenerationofCloudApplications GeorgiosGoumas(NationalTechnicalUniversityofAthens),KonstantinosNikas(ComputingSystemsLaboratory,NTUA),EwnetuBayuhLakew(Dept.ofComputingScience,UmeaUniversity),ChristosKotselidis(TheUniversityofManchester),VasileiosKarakostas(ComputingSystemsLaboratory,NTUA),AtleVesterkjaer(Numascale),EinarRustad(Numascale),JohnGoodacre(Kaleao),AndrewAttwood(Kaleao),MichailFlouris(OnApp),JohnThomson(OnApp),NikosFoutris(TheUniversityofManchester),MikelLujan(TheUniversityofManchester),YingZhang(MonetDBSolutions),PanagiotisKoutsourakis(MonetDBSolutions),MartinKersten(MonetDBSolutions),JimWebber(NeoTechnology),DavideGrohmann(NeoTechnology),ErikElmroth(Dept.ofComputingScience,UmeaUniversity),LuisTomas(Dept.ofComputingScience,UmeaUniversity),NectariosKoziris(NationalTechnicalUniversityofAthens)

Despitetheirproliferationasadominantcomputingparadigm,cloudcomputingsystemslackeffectivemechanismstomanagetheirvastamountsofresourcesefficiently.Resourcesarestrandedandfragmented,ultimatelylimitingcloudsystemsapplicabilitytolargeclassesofcriticalapplicationsthatposenon-moderateresourcedemands.Eliminatingcurrenttechnologicalbarriersofactualfluidityandscalabilityofcloudresourcesisessentialtostrengthencloudcomputingsroleasacriticalcornerstoneforthedigitaleconomy.ACTiCLOUDproposesanovelcloudarchitecturethatbreakstheexistingscale-upandshare-nothingbarriersandenablestheholisticmanagementofphysicalresourcesbothatthelocalcloudsiteandatdistributedlevels.Specifically,itmakesadvancementsinthecloudresourcemanagementstacksbyextendingstate-of-the-arthypervisortechnologybeyondthephysicalserverboundaryandlocalizedcloudmanagementsystemtoprovideanholisticresourcemanagementwithinarack,withinasite,andacrossdistributedcloudsites.Ontopofthis,ACTiCLOUDwilladaptandoptimizesystemlibrariesandruntimes(e.g.,JVM)aswellasACTiCLOUD-nativeapplications,whichareextremelydemanding,andcriticalclassesofapplicationsthatcurrentlyfaceseveredifficultiesinmatchingtheirresourcerequirementstostate-of-the-artcloudofferings.

JointCloud:ACross-CloudCooperationArchitectureforIntegratedInternetServiceCustomization HuaiminWang(NationalUniversityofDefenseTechnology),PeichangShi(NationalUniversityofDefenseTechnology),YimingZhang(NationalUniversityofDefenseTechnology)

CloudcomputinghascompletelychangedtheeconomicsofITindustry.Recently,thenewformofsharedglobaleconomyrequirescloudservicestobecollaborativelyprovisionedbydifferentcloudprovidersinaGeo-distributedmanner,whichbringsseverechallengesinserviceperformanceandcost.Toaddressthisproblem,inthispaperweproposeJointCloud,across-cloudcooperationarchitectureforintegratedInternetservicecustomization.JointCloudborrowstheideafromairlinealliancesandaimsatempoweringthecooperationamongmultiplecloudstoprovideefficientcross-cloudservices.JointCloudfocusesnotonlyontheverticalintegrationofcloudresourcesbutalsoonthehorizontalcooperationamongdifferentcloudvendors.ThispaperdescribestheconceptandarchitectureofJointCloud,aswellastheinitialdesignsofJointCloudskeycomponents,namely,communication,storage,andcomputation.

SupportingDataAnalyticsApplicationsWhichUtilizeCognitiveServices ArunIyengar(IBMResearch)

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AwidevarietyofservicesareavailableovertheWebwhichcandramaticallyimprovethefunctionalityofapplications.Theseservicesincludeinformationretrieval(includingdatalookupsfromavarietyofsourcesandWebsearches),naturallanguageunderstanding,visualrecognition,anddatastorage.Akeyproblemishowtoprovidesupportforapplicationswhichusetheseservices.Thispaperpresentsarichsoftwaredevelopmentkit(SDK)whichaccessestheseservicesandprovidesavarietyoffeaturesapplicationsneedtousetheseservices,optimizeperformance,andcomparethem.AkeyaspectofourSDKisitssupportfornaturallanguageunderstandingservices.WealsopresentapersonalizedknowledgebasebuiltontopofourrichSDKthatusespublicallyavailabledatasourcesaswellasprivateinformation.Theknowledgebasesupportsdataanalysisandreasoningoverdata.

TrillionOperationsKey-ValueStorageEngine:RevisitingtheMissionCriticalAnalyticsStorageSoftwareStack SangeethaSeshadri(IBMAlmadenResearchCenter),LawrenceChiu(IBMAlmadenResearchCenter),PaulMuench(IBMAlmadenResearchCenter)

Dataisthenewnaturalresourceofthiscentury.Asdatavolumesgrowandapplicationsaimedatmonetizingthedatacontinuetoevolve,dataprocessingplatformsareexpectedtomeetnewscale,performance,reliabilityanddataretentionrequirements.Atthesametime,storagehardwarecontinuestoimproveinperformanceandprice-performance.Inthispaper,wepresentTOKVS-TrillionOperationKey-ValueStore,aNoSQLstorageenginethatredefinesthestoragesoftwarestacktomeettherequirementsofnext-generationapplicationsonnext-generationhardware.

Vision4:SecurityandTrustinFutureSystemsHowComputerScienceRiskstoLoseItsInnocence,andShouldAttempttoTakeResponsibilityKarlAberer(EPFL)

Computerscienceisplayingadrivingroleintransformingtoday’ssocietythroughinformationtechnology.Inthistransformationweobservepowershiftsincreasinglystrengtheningcentralisedorganisations,whicharenegativelyperceivedbymanypeople.Weoutlinetechnicalquestionsthatcomputerscienceshouldpayattentiontoinordertoenableindividualsinpreservingtheirinterestandtotakemeaningfuldecisionsbasedonreliableinformation.

ACognitivePolicyFrameworkforNext-GenerationDistributedFederatedSystems-ConceptsandResearchDirections ElisaBertino(PurdueUniversity),SeraphinCalo(IBM),MarounTouma(IBM),DineshVerma(IBM),ChristopherWilliams(UKDSTL),BrianRivera(ArmyResearchLabs)

Next-generationcollaborativeactivitiesandmissionswillbecarriedoutbyautonomousgroupsofdeviceswithalargevarietyofcognitivecapabilities.Thesedeviceswillhavetooperateinenvironmentscharacterizedbyuncertainty,insecurity(bothphysicalandcyber),andinstability.Insuchenvironments,communicationsmaybefragmented.Properpolicy-basedmanagementofsuchautonomousdevicegroupsisthuscritical.Howevercurrentpolicymanagementsystemshavemanylimitations,includinglackofflexibility.Inthispaper,wearticulatenovelarchitecturalapproachesaddressingtherequirementsfortheeffectivemanagementofautonomousgroupsofdevicesanddiscussthenotionofgenerativepoliciesanovelparadigmthatenhancestheflexibilityofpolicy-basedapproachestomanagement.Inthispaper,wealsosurveytypesofpolicythatareessentialformanagingdevicegroups.Eventhoughmanysuchpolicytypesexistinconventionalsettings,theiruseinourcontextposesnovelchallengesthatwearticulateinthepaper.Wealsointroducearesearchroadmapdiscussingseveralresearchdirectionstowardsthedevelopmentofacognitiveandflexiblepolicy-basedapproachtothemanagementofautonomousgroupsofdevicesforcollaborativemissions.Finally,asourproposedpolicyparadigmisdata-intensive,wediscusstheproblemofsupplyingthedatarequiredforpolicydecisionsinenvironmentscharacterizedbymobility,uncertainly,andfragmentedcommunications.

MachinetoMachineTrustinSmartCities MargaretLoper(GeorgiaTechResearchInstitute),BrianSwenson(GeorgiaInstituteofTechnology)

Inthecomingdecades,wewillliveinaworldsurroundedbytensofbillionsofdevicesthatwillinteroperateandcollaborateinanefforttodeliverpersonalizedandautonomicservices.ThisparadigmofsmartobjectsandsmartthingsinterconnectedandubiquitouslysurroundingusiscalledtheInternetofThings(IoT).CitiesmaybethefirsttobenefitfromtheIoT,butrelianceonthesemachinestomakedecisionshasprofoundimplicationsfortrust,andmakesmechanismsforexpressingandreasoningabouttrustessential.ThispaperintroducestheprojectfundedbytheGeorgiaTechResearchInstitutetolookatseveraldimensionsofMachinetoMachineTrustinthecontextofSmartCities.

LateralThinkingforTrustworthyApps HermannHärtig(TechnischeUniversitätDresden),MichaelRoitzsch(TechnischeUniversitätDresden),CarstenWeinhold(TechnischeUniversitätDresden),AdamLackorzynski(TechnischeUniversitätDresden)

Thegrowingcomputerizationofcriticalinfrastructureaswellasthepervasivenessofcomputingineverydaylifehasledtoincreasedinterestinsecureapplicationdevelopment.ExemplifiedbyARMTrustZoneandIntelSGX,weobserveaflurryofnewsecuritytechnologies,butalackofanarchitecturalvision.Weareconvincedthatpointsolutionsarenotsufficienttoaddresstheoverallchallengeofsecuresystemdesign.Inthispaper,wesketchourtakeonatrustedcomponentecosystemofsmallindividualbuildingblockswithstrongisolation.Inourview,applicationsshouldnolongerbedesignedasmassivestacksofverticallylayeredframeworks,butinsteadashorizontalaggregatesofmutuallyisolatedcomponentsthatcollaborateacrossmachineboundariestoprovideaservice.Lateralthinkingisneededtocreatesecuresystemsgoingforward.

RumorInitiatorDetectioninInfectedSignedNetworks JiaweiZhang(UniversityofIllinoisatChicago),CharuC.Aggarwal(IBMT.J.WatsonResearchCenter),PhilipS.Yu(UniversityofIllinoisatChicago)

Inmanycases,theinformationspreadinanonlinenetworkmaynotalwaysbetruthfulorcorrect;suchinformationcorrespondstorumors.Inrecentyears,signednetworkshavebecomeincreasinglypopularbecauseoftheirabilitytorepresentdiverserelationshipssuchasfriends,enemies,trust,anddistrust.Signednetworksareidealforinformationflowinanetworkwithvaryingbeliefs(trustordistrust)aboutfacts.Inthispaper,wewillstudytheproblemofinfluenceanalysisanddiffusionmodelsinsignednetworksandinvestigatetheproblemofrumorinitiatordetection,giventhestateofthenetworkatagivenmomentintime.Conventionalinformationdiffusionmodelsforunsignednetworkscannotbeappliedtosignednetworksdirectly,andweshowthattherumorinitiatordetectionproblemisNP-hard.Weproposeanewinformationdiffusionmodel,referredtoasasyMmetricFlippingCascade(MFC),tomodelthepropagationofinformationinsignednetworks.BasedonMFC,anovelframework,RumorInitiatorDetector(RID),isintroducedtodeterminethepotentialnumberandtheidentityoftherumorinitiatorsfromthestateofthenetworkatagiventime.Extensiveexperimentsconductedonreal-worldsignednetworksdemonstratethatMFCworksverywellinmodelinginformationdiffusioninsignednetworksandRIDcansignificantlyoutperformothercomparisonmethodsinidentifyingrumorinitiators.

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AddressingSmartphone-basedMulti-factorAuthenticationviaHardware-rootedTechnologiesZhongjieBa(TheStateUniversityofNewYorkatBuffalo),KuiRen(TheStateUniversityofNewYorkatBuffalo)

Multi-factorauthenticationisawell-recognizedaccesscontrolmethodthatenhancesthesecurityofuserssensitivedataandidentities.Asuccessfulauthenticationattemptrequiresausertocorrectlypresenttwoormoreauthenticationfactorssuchasknowledgefactors,possessionfactorsandinherencefactors.Forsmartphone-basedmulti-factorauthentication,apromisingwaytoauthenticateauseristoverifyhispossessionofalegitimatesmartphone,whichcallsforsecureandusabledeviceauthenticationschemes.Inthisarticle,weproposetoauthenticateadevicethroughtrackingthehardwarefingerprintofitsbuilt-insensor.Wefirstreviewtheexistinghardwarerootedidentificationmethodsanddiscussthemeritsofapplyingahardwarefingerprintasasmartphonesuniqueidentity.Then,weanalyzethesecurityissuesunderlyingthesemethodsandidentifytwosecurityrequirementsfortheidentificationmethodstobeusedinanauthenticationscheme:FingerprintLeakageResilienceandFingerprintForgeryResilience.Finally,welookintoaspecifichardwarefingerprintoriginallyusedfordigitalcameras.Weanalyzethefeasibilityofapplyingthisfingerprinttodifferentiateoff-the-shelfsmartphonesandlistseveralchallengingpracticalissuesunderlyingthismethod.

Vision5:FutureDistributedSystemsEnablingwideareadataanalyticswithCollaborativeDistributedProcessingPipes(CDPPs) AnjaFeldmann(TUBerlin),ManfredHauswirth(TUBerlin),VolkerMarkl(TUBerlin)

TheMillibottleneckTheoryofPerformanceBugs,andItsExperimentalVerification CaltonPu(GeorgiaInstituteofTechnology),JoshuaKimball(GeorgiaInstituteofTechnology),Chien-AnLai(GeorgiaInstituteofTechnology),TaoZhu(GeorgiaInstituteofTechnology),JackLi,JunheePark,QingyangWang,DeepalJayasinghe,PengchengXiong,SimonMalkowski,QinyiWu,GueyoungJung,YounggyunKoh,GalenSwint

Theperformanceofn-tierweb-facingapplicationsoftensufferfromresponsetimelong-tailproblem.Withrelativelylowresourceutilization(lessthan50%)andthevastmajorityofrequestsreturningwithinafewmilliseconds,anon-negligiblenumberofnormallyshortrequestsmaytakesecondstoreturn.Weproposethemillibottlenecktheoryofperformancebugs(thatleadtolong-tailproblems).Severalcasestudieshaveconfirmedthemillibottlenecks(thatlastafewtenstohundredsofmilliseconds)ascausalagentsoflongrequests.Aconcreteexample(garbagecollection)illustratestheexperimentalverificationofmillibottlenecks.Anopensourcefine-grainmonitoringtoolkitisbeingdevelopedtofacilitatetheexperimentalresearchonmillibottlenecks.

Exacution:EnhancingScientificDataManagementforExascale ScottKlasky(OakRidgeNationalLaboratory),EricSuchyta(OakRidgeNationalLab),MarkAinsworth(BrownUniversity),QingLiu(NewJerseyInstituteofTechnology),BenWhitney(BrownUniversity),MatthewWolf(OakRidgeNationalLaboratory),JongChoi(OakRidgeNationalLaboratory),IanFoster(ArgonneNationalLaboratory),MarkKim(OakRidgeNationalLaboratory),JeremyLogan(UniversityOfTennesseeKnoxville),KshitijMehta(OakRidgeNationalLaboratory),ToddMunson(ArgonneNationalLaboratory),GeorgeOstrouchov(OakRidgeNationalLaboratory),ManishParashar(RutgersUniversity),NorbertPodhorszk(OakRidgeNationalLaboratory),DavidPugmire(OakRidgeNationalLaboratory),LipengWan(OakRidgeNationalLaboratory)

Aswecontinuetowardexascale,scientificdatavolumeiscontinuingtoscaleandbecomingmoreburdensometomanage.Inthispaper,welayoutopportunitiestoenhancestateoftheartdatamanagementtechniques.Weemphasizewell-principleddatacompression,andusingittoachieveprogressiverefinement.ThiscanbothaccelerateI/Oandaffordtheuserincreasedflexibilitywhensheinteractswiththedata.Theformulationnaturallymapsontoenablingonetopartitiontheprogressivelyimprovingqualityrepresentationsofthesamedataquantityintodifferentmedia-typedestinations,tokeepthehighestpriorityinformationascloseaspossibletothecomputation,andtakeadvantageofdeepeningmemory/storagehierarchiesinwaysnotpreviouslypossible.Carefulmonitoringisrequisitetoourvision,notonlytoverifythatcompressionhasnoteliminatedsalientfeaturesinthedata,butalsotobetterunderstandtheperformanceofhighperformancescientificapplications.Increasedmathematicalrigorwouldideal,tohelpbringcompressiononabetter-understoodtheoreticalfooting,closertotherelevantscientifictheory,moreawareofconstraintsimposedbythescience,andmoretightlyerrorcontrolled.Throughout,wehighlightpathfindingresearchwehavebegunexploringrelatedthesetopics,andcommenttowardfutureworkthatwillbeneeded.

HardwareAccelerationLandscapeforDistributedReal-timeAnalytics:VirtuesandLimitations MohammadrezaNajafi(TechnischeUniversitatMunchen),KaiwenZhang(TechnischeUniversitatMunchen),Hans-ArnoJacobsen(UniversityofToronto),MohammadSadoghi(PurdueUniversity)

Arguably,wearenowwitnessinganewtechnologicalrevolutionwiththepotentialthatrangesfromtransformingourday-to-daylifeexperiences(e.g.,personalizedmedicineandeducation)totransformingeverysingleindustry(e.g.,data-drivenhealthcare,commerce,agriculture,andmining).Atthecoreofthisrevolutionliesdata.Thistransformationisfacilitatedbysensing,gathering,andconnectingallphysicalentitiestoconstructarichanddynamiccomputationalmodelofrealityinreal-time.Everyprocedureandeverydecisionneededinthephysicalworldwillsoonbeoptimizedinreal-timebyingestingandanalyzingmassivevolumeofpresentandpastdataatanunprecedentedvelocity.Tocopewithsuchextremescale,wearguetheneedtorevisitthehardwareandsoftwareco-designinlightoftwokeytechnologicaladvancements.Firstisthevirtualizationofcomputationandstorageoverhighlydistributeddatacentersspanningacrosscontinents.Secondistheemergenceofavarietyofspecializedhardwareacceleratorsthatcomplementthetraditionalgeneral-purposeprocessors.Wearguethereisanimminentneedtoexploitandunifythesetwotrendsinordertounleashandharnessthepowerofdatainreal-time.Inthispaper,wefocusonpresentingaformulationandcharacterizationofhardwareaccelerationlandscapegearedtowardsreal-timeanalyticsinthecloudenvironment.Ourgoalistoassistbothresearchersandpractitionersnavigatingthenewlyrevivedfieldofsoftwareandhardwareco-designforbuildingnextgenerationdistributedsystems.Wefurtherpresentacasestudytoexploresoftwareandhardwareinterplayfordesigningdistributedrealtimestreamprocessing.

CoordinatingDistributedSpeakingObjects MarcoLippi(DISMI–UniversitàdiModenaeReggioEmilia),MarcoMamei(DISMI–UniversitàdiModenaeReggioEmilia),StefanoMariani(DISMI–UniversitàdiModenaeReggioEmilia),FrancoZambonelli(DISMI–UniversitàdiModenaeReggioEmilia)

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Inthispaperwesketchavisionoffutureenvironmentsdenselypopulatedbysmartsensorsandactuatorspossiblyembeddedineverydayobjectsthat,ratherthansimplyproducingstreamsofdata,arecapableofunderstandingandreporting,viafactualassertionsandarguments,aboutwhatishappening(forsensors)andaboutwhattheycanmakepossiblyhappen(foractuators).Thesespeakingobjectsformthenodesofadensedistributedcomputinginfrastructurethatcanbeexploitedtomonitorandcontrolactivitiesinoureverydayenvironment.However,thenatureofspeakingobjectswilldramaticallychangetheapproachestoimplementingandcoordinatingtheactivitiesofdistributedprocesses.Infact,distributedcoordinationislikelytobecomeassociatedwiththecapabilityofargumentingaboutsituationsandaboutthecurrentstateoftheaffairs,withtheaimoftriggeringanddirectingproperdistributedconversationstocollectivelyreachafuturedesirablestateoftheaffairs.Inthispaperwediscusshowsuchanovelvisioncanbuilduponsomereadilyavailabletechnologies,andwehighlighttheresearchchallengesthatitposes.Twocasestudiesareusedthroughoutthepaperasexemplaryscenarios.

Model-DrivenDomain-SpecificMiddleware FabioCosta(FederalUniversityofGoias),KarlMorris(TempleUniversity),FabioKon(UniversityofSãoPaulo),PeterClarke(FloridaInternationalUniversity)

Middlewarewasintroducedtofacilitatethedevelopmentofsophisticatedapplicationsbasedonauniformmethodologyandindustrystandards.However,earlyresearchandpracticesuggestedthatnoone-size-fits-allapproachwassuitableforallapplicationdomainsandscenarios.Thisgaverisetoindustryinitiativestostandardizedomain-specificmiddlewareservicesandprofiles,aswellasresearcheffortsonconfigurable,reflective,andadaptivemiddleware.Theindustrysapproachledtoeasydeployment,althoughwithalevelofflexibilitylimitedbytheextentofexistingprofiles.Theapproachoftheresearchcommunity,ontheotherhand,enabledhighflexibility,allowinganymiddlewareconfigurationtobedefined.Nevertheless,creatingsoundconfigurationsusingthisapproachisachallengingtask,limitingthetargetaudiencetoexpertengineers.Asaconsequence,bothinitiativesdonotscalewiththecurrentproliferationofspecializedapplicationdomains.Inthispaper,wetargetthisproblemwithanapproachthatleveragesmodel-drivenengineeringfortheconstructionofdomain-specificmiddlewareplatforms.Asetofhigh-level,yetexpressive,buildingblocksisdefinedintheformofametamodel,whichisusedtocreatemodelsthatspecifythedesiredmiddlewareconfiguration.Wearguethatthisapproachenablestherapiddevelopmentofmiddlewareplatformstomatchtheproliferationofapplicationdomains,atthesametimeasitdoesnotrequireper-applicationmiddlewareconstructionorevenhighlyskilledmiddlewareengineers.Wepresentthecurrentstateofourresearchanddiscussresearchdirectionstofullyrealizetheapproach.

Vision6:InnovationinBigDataSystemsOntheDesignofaBlockchainPlatformforClinicalTrialandPrecisionMedicine ZonyinShae(ASIAUniversity,Taiwan),JeffreyTsai(ASIAUniversity,Taiwan)

Thispaperproposesablockchainplatformarchitectureforclinicaltrialandprecisionmedicineanddiscussesvariousdesignaspectsandprovidessomeinsightsinthetechnologyrequirementsandchallenges.Weidentify4newsystemarchitecturecomponentsthatarerequiredtobebuiltontopoftraditionalblockchainanddiscusstheirtechnologychallengesinourblockchainplatform:(a)anewblockchainbasedgeneraldistributedandparallelcomputingparadigmcomponenttodeviseandstudyparallelcomputingmethodologyforbigdataanalytics,(b)blockchainapplicationdatamanagementcomponentfordataintegrity,bigdataintegration,andintegratingdisparityofmedicalrelateddata,(c)verifiableanonymousidentitymanagementcomponentforidentityprivacyforbothpersonandInternetofThings(IoT)devicesandsecuredataaccesstomakepossibleofthepatientcentricmedicine,and(d)trustdatasharingmanagementcomponenttoenableatrustmedicaldataecosystemforcollaborativeresearch.

TowardsDataflow-basedGraphAccelerator HaiJin(HuazhongUniversityofScienceandTechnology),PengchengYao(HuazhongUniversityofScienceandTechnology),XiaofeiLiao(HuazhongUniversityofScienceandTechnology),LongZheng(HuazhongUniversityofScienceandTechnology),XianliangLi(HuazhongUniversityofScienceandTechnology)

Existinggraphprocessingframeworksgreatlyimprovetheperformanceofmemorysubsystem,buttheyarestillsubjecttotheunderlyingmodernprocessor,resultinginthepotentialinefficienciesforgraphprocessinginthesenseoflowinstructionlevelparallelismandhighbranchmisprediction.Theseinefficiencies,inaccordancewithourcomprehensivemicro-architecturalstudy,mainlyariseoutofawealthofdatadependencies,serialsemanticofinstructionstreams,andcomplexconditionalinstructionsingraphprocessing.Inthispaper,weproposethatafundamentalshiftofapproachisnecessarytobreakthroughtheinefficienciesoftheunderlyingprocessorviathedataflowparadigm.Itisverifiedthattheideaofapplyingdataflowapproachintographprocessingisextremelyappealingforthefollowingtworeasons.First,astheexecutionandretirementofinstructionsonlydependontheavailabilityofinputdataindataflowmodel,ahighdegreeofparallelismcanbethereforeprovidedtorelaxtheheavydependencyandserialsemantic.Second,dataflowisguaranteedtomakeitpossibletoreducethecostsofbranchmispredictionbysimultaneouslyexecutingallbranchesofaconditionalinstruction.Consequently,wemakethepreliminaryattempttodevelopthedataflowinsightintoaspecializedgraphaccelerator.Webelievethatourworkwouldopenawiderangeofopportunitiestoimproveperformanceofcomputationandmemoryaccessforlarge-scalegraphprocessing.

TowardsaRISCFrameworkforEfficientContextualizationinIoT DimitriosGeorgakopoulos(SwinburneUniversity),AliYavari(RMITUniversity),PremPrakashJayaraman(SwinburneUniversity),RajivRanjan(NewcastleUniversity)

TheInternetofThings(IoT)isanewinternetevolutionthatinvolvesconnectingbillionsofinternet-connecteddeviceswerefertoasIoTthings.ThesedevicescancommunicatedirectlyandintelligentlyovertheInternet,andgenerateamassiveamountofdatathatneedstobebyavarietyofIoTapplications.ThispaperfocusesontheautomaticcontextualisationofIoTdata,whichalsoinvolvesdistillinginformationandknowledgefromIoTaimingtosimplifyansweringthefollowingfundamentalquestionsthatoftenariseinIoTapplications:WhichdatacollectedbyIoTarerelevanttomyselfandtheIoTThingsIcarefor?Relatedworkaroundcontextmanagementandcontextualisationrangesfromdatabasetechniquesthatinvolvequeryre-writing,tosemanticwebandrule-basedcontextmanagementapproaches,tomachinelearninganddatascience-basedsolutionsinmobileandambientcomputing.Allsuchexistingapproacheshavetwomainaspectsincommon:Theyarehighlyincompatibleandhorriblyinefficientfromascalabilityandperformanceperspective.Inthispaper,wediscussanewRISCContextualisationFramework(RCF)wehavedeveloped,implementedkeyaspectoff,andassessesitsscalability.RCFprovidesfundamentalcontextualisationconceptsthatcanbemappedtoallexistingcontextualisationapproachesforIoTdata(andinthissense,itprovidesacommondenominatorthatunifiesthecontextualisationspace).RCFcanbeeasilyimplementedasacloud-basedservice,andprovidesbetterscalabilityandperformancethatanyoftheexistingcontentmanagementandcontextualisationapproachintheIoTspace.

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TheFutureoftheSemanticWeb:PrototypesonaGlobalDistributedFilesystem MichaelCochez(Fraunhofer-FIT),DominikHüser(RWTHAachenUniversity),StefanDecker(RWTHAachen)

Recently,prototypes(inthemeaningfamiliarfromprogramminglanguagessuchasJavascript)were(re-)introducedforknowledgerepresentationontheweb.However,thatworkhasaverytheoreticalfocusandamorepracticalsystemisindemand.Inthisvisionpaperwedescribehowadistributedfilesystemformsanaturalhabitatfortheprototypeknowledgerepresentation.Inparticular,wedescribehowweenvisiondeploymentofLinkedDataandPrototypeKnowledgebasesatopoftheInterPlanetaryFileSystem(IPFS),whichhasseveralusefulfeaturesmatchingtheneedsfortheprototypesystem.

OnBroadBigData SteffenStaab(InstitutWeST,UniversityKoblenz-LandauandWAIS,UniversityofSouthampton)

Abroaddatasystemexplicitlyrepresentsalargenumberofconceptsandpropertiestogetherwithitscorrespondingdataproper.Weobservethecharacteristicsofseveralbroaddatasystemsandelicitthreechallengeswewillneedtoresearchwhenscalingthesebroaddatasystemstobecomebigdatasystems,too.

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Short Paper Abstracts

ShortPaper1:DistributedOperatingSystems,Middleware,andAlgorithmsSRLB:ThePowerofChoicesinLoadBalancingwithSegmentRoutingYoannDesmouceaux(ÉcolePolytechnique),PierrePfister(CiscoSystems),JérômeTollet(CiscoSystems),MarkTownsley(CiscoSystems),ThomasClausen(ÉcolePolytechnique)

Networkload-balancersgenerallyeitherdonottakeapplicationstateintoaccount,ordosoatthecostofacentralizedmonitoringsystem.Thispaperintroducesaload-balancerrunningexclusivelywithintheIPforwardingplane,i.e.inanapplicationprotocolagnosticfashion–yetwhichstillprovidesapplication-awarenessandmakesreal-time,decentralizeddecisions.Tothatend,IPv6SegmentRoutingisusedtodirectdatapacketsfromanewflowthroughachainofcandidateservers,untilonedecidestoaccepttheconnection,basedonitslocalstate.Thisway,applicationsthemselvesnaturallydecideonhowtoshareincomingconnections,whileincurringminimalnetworkoverhead,andnoout-of-bandsignaling.Testsondifferentworkloads–includingrealisticworkloadssuchasreplayingactualWikipediaaccesstraffictowardsasetofreplicaWikipediainstances–showsignificantperformancebenefits,intermsofshorterresponsetimes,whencomparedtoatraditionalrandomload-balancer.

ImprovingEfficiencyofLinkClusteringonMulti-CoreMachines GuanhuaYan(BinghamtonUniversity)

Linkclusteringgroupsdifferentedgesinagraphaccordingtotheirsimilarities.Linkclusteringcanrevealtheoverlappingandhierarchicalorganizationsinawidespectrumofnetworks.Thisworkstudieshowtoimproveefficiencyoflinkclusteringalongthreedimensions,algorithm,modeling,andparallelization,onmulti-coremachines.Weevaluatetheefficiencyimprovedduetoeachofthethreedimensionsusingwordassociationgraphsextractedfromatwitterdataset.

S3:JointSchedulingandSourceSelectionforBackgroundTrafficinErasure-CodedStorage ShijingLi(GeorgeWashingtonUniversity),TianLan(GeorgeWashingtonUniversity),Moo-RyongRa(AT&TLabsResearch),RajeshPanta(AT&TLabsResearch)

Erasure-codedstoragesystemshavegainedconsiderableadoptionrecentlysincetheycanprovidethesamelevelofreliabilitywithsignificantlylowerstorageoverheadcomparedtoreplicatedsystems.However,backgroundtrafficofsuchsystems–e.g.repair,rebalance,backupandrecoverytraffic–oftenhaslargevolumeandconsumessignificantnetworkresources.Independentlyschedulingsuchtasksandselectingtheirsourcescaneasilycreateinterferenceamongdataflows,causingseveredeadlineviolation.Weshowthatthewell-knownheuristicschedulingalgorithmsfailtoconsiderimportantconstraints,thusresultinginunsatisfactoryperformance.Inthispaper,weclaimthatanoptimalschedulingalgorithmthataimstomaximizethenumberofbackgroundtaskscompletedbeforedeadlinesmustsimultaneouslyconsiderdeadline-awarescheduling,networktopology,chunkplacement,andtime-varyingresourceavailability.Tosolvethisproblem,weproposeanovelalgorithm,calledLinearProgrammingforSelectedTasks(LPST)tomaximizethenumberofsuccessfultasksandimproveoverallutilizationofthedatacenternetwork.ItjointlyschedulestasksandselectstheirsourcesbasedonanotionofRemainingTimeFlexibility,whichmeasurestheslacknessofthestartingtimeofatask.Weevaluatedtheefficacyofouralgorithmusingextensivesimulations.Ourresultsshowthat,undercertainscenarios,LPSTcanperform7x∼70xbetterthantheheuristicswhichblindlytreattheinfrastructureasacollectionofhomogeneousresources,and46.6%∼65.9%betterthanthealgorithmsthattakeintoaccountthenetworktopology.

OntheFeasibilityofInter-domainRoutingviaaSmallBrokerSet DongLin(HuaweiTechnologiesLtdCo.),DavidHui(HuaweiTechnologiesLtdCo.),WeijieWu(HuaweiTechnologiesLtdCo.),TingweiLiu(TheChineseUniversityofHongKong),YatingYang(BeijingInstituteofTechnology),YiWang(TsinghuaUniversity),JohnChi-ShingLui(ChineseUniversityofHongKong),GongZhang(HuaweiTechnologiesLtdCo.),YingtaoLi(HuaweiTechnologiesLtdCo.)

Thecurrentinter-domainroutingprotocol,namely,theBorderGatewayProtocol(BGP),cannotprovideend-to-end(E2E)quality-of-service(QoS)guarantees.Themainreasonisthatanautonomoussystem(AS)canonlyreceiveguaranteesfromitsfirsthopASesviaservicelevelagreements(SLAs).Butbeyondthefirsthop,QoSalongthepathfromsourcetodestinationASisnotwithinthesourceAS’scontrolregime.Inthispaper,weinvestigatethefeasibilityofprovidinghighQoS-guaranteedE2Etransitservicesbyutilizinga(small)setofASes/IXPstoserveas“brokers”toprovidesupervision,controlandresourcenegotiation.FindinganoptimalsetofASesasbrokerscanbeformulatedasaMaximumCoveragewithB-dominatingpathGuarantee(MCBG)problem,whichweprovetobeNP-hard.Toaddressthisproblem,wedesigna(1-e-1/4)-approximationalgorithmandalsoanefficientheuristicalgorithmwhenconsideringadditionalconstraints(e.g.,pathlength).BasedonthecurrentInternettopology,wediscovera“3540-alliance”subset(accountingonly6.8%)of52,079ASes/IXPs,whichcanprovidehighQoSguaranteesfor99.29%E2Econnections.

SubscriptionCoveringforRelevance-basedFilteringinContent-BasedPublish/SubscribeSystems KaiwenZhang(TechnischeUniversitatMunchen),VinodMuthusamy(IBMResearch),MohammadSadoghi(PurdueUniversity),Hans-ArnoJacobsen(UniversityofToronto)

Large-scaleapplicationsrequireascalabledatadisseminationservicewithadvancedfilteringcapabilities.Weproposetheuseofacontent-basedpublish/subscribesystemwithsupportfortop-kfilteringinthecontextofsuchapplications.Wefocusontheproblemoftop-ksubscriptionfiltering,whereapublicationisdeliveredonlytothekhighestscoringsubscribers.Thenaiveapproachtoperformfilteringearlyatthepublisheredgeworksonlyifcompleteknowledgeofthesubscriptionsisavailable,whichisnotcompatiblewiththewell-establishedcoveringoptimizationinscalablecontent-basedpublish/subscribesystems.Weproposeanefficientrank-covertechniquetoreconciletop-ksubscriptionfilteringwithcovering.Weextendthecoveringmodeltosupporttop-kanddescribeanovelalgorithmforforwardingsubscriptionstopublisherswhilemaintainingcorrectness.Finally,wecompareoursolutionstoabaselinecoveringsystem.Inatypicalsetting,ouroptimizedsolutionisscalableandprovidesover81%ofthecoveringbenefit.

WorkflowOptimizationinPAW MaximFilatov(UNIGE),VerenaKantere(UniversityofGeneva)

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Manyindustrialapplications,fromdomainssuchastelecommunication,webandsales,requiretoperformcomplexanalyticsacrossseveraldataprocessingsystems.Theperformanceofsuchanalyticsisusuallyexpressedinworkflows,anditisataskthatisbothlabor-intensiveandtime-consuming.Atthesametime,withincreasingamountsofdatatobeanalysed,theoptimizationofanalyticsworkflowsbecomescrucialforsatisfyingbusinessobjectives.Thispaperfocusesonworkflowoptimizationwithrespecttotimeefficiency,overmultipleexecutionengines,suchasatraditionalDBMS,aMapReduceengine,andascriptingengine.Thisconfigurationisemergingasacommonparadigmusedtocombineanalysisofunstructuredandstructureddata.WeproposeanoveloptimizationtechniqueaspartofoursystemcalledPAW(PlatformforAnalyticsWorkflows).Thistechniquecreatesalternativeworkflowstructuresandtheirexecutionplansbasedonequivalentcombinationsandordersofoperators.Thetechniqueemploysanexhaustiveandaheuristicalgorithmtosearchefficientlythespaceofequivalentworkflowstructuresandselecttheonewiththeoptimalexecutionplan.Wepresentathoroughexperimentalstudyandweshowcasetheefficiencyoftheproposedoptimizationtechniqueinafullyfledgedmulti-enginesystem,appliedonthreereal-worldapplicationsandtheirdata,aswellasonasyntheticbenchmark.

AFirstLookatInformationEntropy-BasedDataPricing XijunLi(ShanghaiJiaoTongUniversity),JianguoYao(ShanghaiJiaoTongUniversity),XueLiu(McGillUniversity),HaibingGuan(ShanghaiJiaoTongUniversity)

Distributionofintangibleinformationgoodsisexperiencingtremendousgrowthinrecentyears,whichhasfacilitatedablossomingofinformationgoodseconomics.Asbigdatadevelops,therearemoreandmoreinformationgoodsmarketsfordatatrading.Inthecurrentofdatapricingpoliciesindatatrading,therearemanymetricstomeasurethevalueofdatagoods,suchasthedatagenerationdate,datavolume,anddataintegrity,etc.However,itisverychallengingtoidentifytheamountofdatainformationanditsdistribution,andthecorrespondingdatapricinghasrarelybeendiscussed.Inthispaper,weproposeanewdatapricingmetric,i.e.,thedatainformationentropy,whichhelpstomakeareasonablepriceinthedatatrading.Wefirstdemonstrateadatainformationmeasurementmethodbasedoninformationentropy,andthenproposeapricingfunctionbasedontheresultofdatainformationmeasurement.Tocomprehensivelyunderstandthenewdatapricingmetricandfacilitateitsapplicationindatatrading,weverifytherationalityofthedatainformationmeasurementmethodandgivethreeconcretepricingfunctions.Itisthefirsttimetolookattheinformationentropy-baseddatapricing,whichcaninspiretheresearchconcerningthepricingmechanismofdatagoods,furtherpromotingthedevelopmentofdataproductsbusiness.

RestrospectiveLightweightDistributedSnapshotsUsingLooselySynchronizedClocks AlekseyCharapko(SUNYBuffalo),AilidaniAilijiang(SUNYBuffalo),MuratDemirbas(SUNYBuffalo),SandeepKulkarni(MichiganStateUniversity)

Inordertotakeaconsistentsnapshotofadistributedsystem,itisnecessarytocollateandalignlocallogsfromeachnodetoconstructapairwiseconcurrentcut.ByleveragingNTPsynchronizedclocks,andaugmentingthemwithlogicalclockcausalityinformation,Retroscopeprovidesalightweightsolutionfortakingunplannedretrospectivesnapshotsofpastdistributedsystemstates.Insteadofstoringamultiversioncopyoftheentiresystemdata,thisisachievedefficientlybymaintainingaconfigurable-sizeslidingwindow-logateachnodetocapturerecentoperations.Inadditiontoretrospectivesnapshots,Retroscopealsoprovidesincrementalandrollingsnapshotsthatutilizeanexistingsnapshottoreducethecostofconstructinganewsnapshotinproximity.Thiscapabilityisusefulforperformingstepwisedebuggingandroot-causeanalysis,andsupportingdataintegritymonitoringandcheckpoint-recovery.WeimplementRetroscopefortheVoldemortdistributeddatastoreandevaluateitsperformanceundervaryingworkloads.

Power-AwarePopulationProtocols ChuanXu(LRI(CNRS/UPSud)),JannaBurman(LRI(CNRS/UPSud)),JoffroyBeauquier(LRI(CNRS/UPSud))

Inthispaper,weproposeaformalenergymodelwhichallowsananalyticalstudyofenergyconsumption,forthefirsttimeinthecontextofpopulationprotocols(PP).InPP,anonymousandboundedmemoryagentsmoveunpredictablyandcommunicateinpairs.Inordertoillustratethepowerandtheusefulnessoftheproposedenergymodel,wedevelopanewpower-awareprotocol(EB-TTFM)forthetaskofdatacollection.Theanalyticalresultsshowthat,intermsofenergyconsumption,EB-TTFMoutperformsaknowndatacollectionprotocolundercertainconditions.Finally,wepresentalowerboundconcerningenergyconsumptionofanypossibledatacollectionprotocolinPP,whichalsojustifiestheefficiencyofEB-TTFM.

MultiPub:LatencyandCost-AwareGlobal-ScaleCloudPublish/Subscribe JulienGascon-Samson(McGillUniversity),JörgKienzle(McGillUniversity),BettinaKemme(McGillUniversity)

Topic-basedpub/subisawidelyusedcommunicationmechanismindistributedsystemsfortargetedinformationdisseminationbetweenlooselycoupledentities.Toscaledynamicallydependingonthecurrentcommunicationdemands,pub/servicescanbeconvenientlydeployedinthecloud.Toprovidefastdissemination,theservicecanbedistributedacrossmultiplecloudregions.Thearchitecturaldesignandrun-timedeploymentofsuchamiddlewareistricky,though,asitcanhaveasignificanteffectoncommunicationlatencyandcloud-basedcost.Inthispaper,weproposeMultiPub,aflexiblepub/submiddlewareforlatency-constrained,world-widedistributedapplicationsthatdynamicallyreconfiguresthecommunicationlayertoensureapredefinedmaximumlatencyforpublicationdisseminationwhileminimizingcloud-basedcosts.Thisisachievedbyroutingpublicationseitherthroughasingleoracrossmultiplecloudregions.WedemonstratetheeffectivenessofMultiPubbypresentingasetofexperimentsthatreportontheachievedcommunicationlatencyandcostsavingscomparedtotraditionalapproaches,aswellasaperformanceevaluation.

ReachabilityinBinaryMultithreadedProgramsIsPolynomial AlexanderMalkis(TechnischeUniversitätMünchen),SteffenBorgwardt(UCDavis)

Automaticfindingofbugsinmultithreadedprogramsisanimportantbutinherentlydifficulttask,eveninthefinite-stateinterleaving-semanticscase.Thecomplexityofthistaskhasonlybeenpartiallyexploredsofar.Wemeasurequantitiessuchasthediameter,whichisthelongestfinitedistancerealizableinthetransitiongraphoftheprogram,thelocaldiameter,whichisthemaximumdistancefromanyprogramstatetoanythread-localstate,andthecomputationalcomplexityofbugfinding.Forthesubclassofso-calledbinarymultithreadedprograms,weprovenewbounds:allthesequantitiesaremajorizedbyapolynomialand,incertaincases,byalinear,logarithmic,orevenconstantfunction.Ourboundspresentapreparationsteptowardsthecorrespondingpolynomial-boundclaimsforgeneralprograms.Theseclaimscontrastsharplywiththecommonbeliefthatthemainobstacletoanalyzingconcurrentprogramsistheexponentialstateexplosioninthenumberofthreads.

AnEvent-LevelAbstractionforAchievingEfficiencyandFairnessinNetworkUpdate

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TingQu(NationalUniversityofDefenseTechnology),DekeGuo(NationalUniversityofDefenseTechnology),XiaominZhu(NationalUniversityofDefenseTechnology),JieWu(TempleUniversity),XiaoleiZhou(NationalUniversityofDefenseTechnology),ZhongLiu(NationalUniversityofDefenseTechnology)

Changesofnetworkstateareacommonsourceofinstabilityinnetworks.Anupdateeventtypicallyinvolvesmultipleflowsthatcompetefornetworkresourcesatthecostofreschedulingandmigratingsomeexistingflows.Priornetworkupdatingschemestacklesuchflowsindependently,ratherthanastheentityofanupdateevent.Theyonlyoptimizetheflow-levelmetricsfortheflowsinvolvedinanupdateevent.Inthispaper,wepresentanevent-levelabstractionofnetworkupdatewhichgroupsflowsofanupdateeventandschedulesthemtogethertominimizetheeventcompletiontime(ECT).Wethenstudytheschedulingproblemofmultipleupdateeventsforachievinghighschedulingefficiencyandpreservingfairness.Thedesignedleastmigrationtrafficfirst(LMTF)methodschedulesallupdateeventsintheFIFOorder,butavoidshead-of-lineblockingbyrandomlyfine-tuningthequeueorderofsomeevents.Itcanconsiderablyreducetheupdatecost,theaverage,andtailECTsofallupdateevents.Inaddition,wedesignageneralparallel-LMTF(P-LMTF)methodtoguaranteefairnessandfurtherimproveschedulingefficiencyamongupdateevents.ItimprovestheLMTFmethodbyopportunisticallyupdatingmultipleeventssimultaneously.ThecomprehensiveevaluationresultsindicatethattheaverageECTofourapproachisupto10×fasterthantheflow-levelschedulingmethodfornetworkupdateevents,anditstailECTisupto6×faster.OurP-LMTFmethodincurs75%reductionintheaverageECTcomparedwithFIFOwhenthenetworkutilizationexceeds70%,anditachievesa42%reductionintailECT.

ShortPaper2:CloudandDataCenterSystemsandNetworksDCM:DynamicConcurrencyManagementforScalingn-TierApplicationsinCloud HuiChen(LouisianaStateUniversity),QingyangWang(LouisianaStateuniversity),BalajiPalanisamy(UniversityofPittsburgh),PengchengXiong(Hortonworks)

Scalingwebapplicationssuchase-commerceincloudbyaddingorremovingserversinthesystemisanimportantpracticetohandleworkloadvariations,withthegoalofachievingbothhighqualityofservice(QoS)andhighresourceefficiency.Throughextensivescalingexperimentsofann-tierapplicationbenchmark(RUBBoS),wehaveobservedthatscalingonlyhardwareresourceswithoutappropriateadaptationofsoftresourceallocations(e.g.,threadorconnectionpoolsize)ofeachserverwouldcausesignificantperformancedegradationoftheoverallsystembyeitherunder-orover-utilizingthebottleneckresourceinthesystem.Wedevelopadynamicconcurrencymanagement(DCM)frameworkwhichintegratessoftresourceallocationsintothesystemscalingmanagement.DCMintroducesamodelwhichdeterminesanear-optimalconcurrencysettingtoeachtierofthesystembasedonacombinationofoperationalqueuinglawsandonlineanalysisoffine-grainedmeasurementdata.WeimplementDCMasatwo-levelactuatorwhichscalesbothhardwareandsoftresourcesinann-tiersystemontheflywithoutinterruptingtheruntimesystemperformance.OurexperimentalresultsdemonstratethatDCMcanachievesignificantlymorestableperformanceandhigherresourceefficiencycomparedtothestate-of-the-arthardware-onlyscalingsolutions(e.g.,AmazonEC2-AutoScale)underrealisticburstyworkloadtraces.

MorePeak,LessDifferentiation:TowardsAPricing-awareOnlineControlFrameworkforInter-DatacenterTransfers WenxinLi(DalianUniversityofTechnology),XiaoboZhou(TiajjinUniversity),KeqiuLi(DalianUniversityofTechnology),HengQi(DalianUniversityofTechnology),DekeGuo(NationalUniversityofDefenceTechnology)

Theemergingdeploymentofgeographicallydistributeddatacenters(DCs)incursasignificantamountofdatatransfersovertheInternet.SuchtransfersaretypicallychargedbyInternetServiceProviders(ISPs)withthewidelyadoptedq-thpercentilechargingmodel.Insuchchargingmodel,thetimeslotswithtop100-qpercentofdatatransmissiondonotaffectthetotaltransmissioncost,andcanbeviewedasfree.Thisbringstheopportunitytooptimizetheschedulingofinter-DCtransferstominimizetheentiretransmissioncost.However,verylittleworkhasbeendonetoexploitthosefreetimeslotsforschedulinginter-DCtransfers.Thecruxisthatexistingworkeitherlacksamechanismtoaccumulatetraffictofreetimeslots,orinevitablyreliesonpriorknowledgeoftrafficarrivalpatterns.Inthispaper,weattempttoexploitthosefreetimeslotsbyleveragingdiversetime-sensitivitiesamonginter-DCtransfers,soastoreduceorevenminimizethetransmissioncost.Specifically,weadvocatethatasimpleprincipleshouldbefollowed:moretrafficpeaksshouldbescheduledinfreetimeslots,whilelesstrafficdifferentiationshouldbemaintainedamongtheremainingtimeslots.Tothisend,wetakeadvantageoftheLyapunovoptimizationtechniquestodesignapricing-awarecontrolframework.Thisframeworkefficientlymakesonlinedecisionsforinter-DCtransferswithoutrequiringapriorknowledgeoftrafficarrivals.Toverifyourproposedframework,weconductsmall-scaletestbedimplementation.Theresultsshowthatourframeworkcanrealisticallyreducethetransmissioncostbyupto19.38%.

RobustMulti-TenantServerConsolidationintheCloudforDataAnalyticsWorkloads JosephMate(UniversityofWaterloo),KhuzaimaDaudjee(UniversityofWaterloo),ShahinKamali(MITCSAIL)

Serverconsolidationistheallocationorhostingoftenantsonaminimumnumberofcloudservermachines.Givenasequenceofdataanalyticstenantloadsdefinedbytheamountofresourcesthatthetenantsrequireandaservice-levelagreement(SLA)betweenthecustomerandthecloudserviceprovider,significantresourcecostsavingscanbeachievedbyconsolidatingmultipletenantsonservermachines.Sinceservermachinescanfailcausingtheirtenantstobecomeunavailable,serviceproviderscanplacereplicasofeachtenantondifferentserversandreservecapacitytoensurethattenantfailoverwillnotresultinoverloadonanyremainingserver.WeproposetheCUBEFITalgorithmformulti-tenantserverconsolidationthatsavesresourcecostsbyutilizingfewerserversthanexistingapproachesfordataanalyticsworkloads.Unlikeexistingconsolidationalgorithms,CUBEFITcantoleratemultipleserverfailureswhileensuringthatnoserverbecomesoverloaded.WeprovideextensivetheoreticalanalysisandexperimentalevaluationofCUBEFIT.Weshowthatcomparedtoexistingalgorithms,theaveragecaseandworstcasebehaviorofCUBEFITissuperiorandthatitproducesnear-optimaltenantallocationwhenthenumberoftenantsislarge.Throughevaluationanddeploymentonaclusterofupto73machinesaswellasthroughsimulationstudies,weexperimentallydemonstratetheefficacyofCUBEFIT.

Flow-AwareAdaptivePacingtoMitigateTCPIncastinDatacenterNetworks ShaojunZou(CentralSouthUniversity),JiaweiHuang(CentralSouthUniversity),YutaoZhou(CentralSouthUniversity),JianxinWang(CentralSouthUniversity),TianHe(UniversityofMinnesota)

Indatacenternetworks,manynetwork-intensiveapplicationsleveragelargefan-inandmany-to-onecommunicationtoachievehighperformance.However,thespecialtrafficpatterns,suchasmicro-burstandhighconcurrency,easilycauseTCPIncastproblemandseriouslydegradetheapplicationperformance.ToaddresstheTCPIncastproblem,wefirstrevealtheoreticallyandempiricallythatalleviatingpacketburstinessismuchmoreeffectiveinreducingtheIncastprobabilitythancontrollingthecongestionwindow.Inspiredbythefindingsandinsightsfromourexperimentalobservations,wefurtherproposeageneralsupportingschemeAdaptivePacing(AP),whichdynamicallyadjustsburstinessaccordingtotheflowconcurrencywithoutanychangeonswitch.AnotherfeatureofAPisitsbroad

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applicability.WeintegrateAPtransparentlyintodifferentTCPprotocols(i.e.,DCTCP,L2DCTandD2TCP).Throughaseriesoflarge-scaleNS2simulations,weshowthatAPsignificantlyreducestheIncastprobabilityacrossdifferentTCPprotocolsandthenetworkgoodputcanbeincreasedconsistentlybyonaverage7xunderseverecongestion.

Real-TimePowerCyclinginVideoonDemandDataCentresusingOnlineBayesianPrediction VicentSanzMarco(LancasterUniversity),ZhengWang(LancasterUniversity),BarryPorter(LancasterUniversity)

Energyusageindatacentrescontinuestobeamajorandgrowingconcernasanincreasingnumberofeverydayservicesdependonthesefacilities.Researchinthisareahasexaminedtopicsincludingpowersmoothingusingbatteriesanddeeplearningtocontrolcoolingsystems,inadditiontooptimisationtechniquesforthesoftwarerunninginsidedatacentres.Wepresentanovelreal-timepower-cyclingarchitecture,supportedbyamediadistributionapproachandonlinepredictionmodel,toautomaticallydeterminewhenserversareneededbasedondemand.Wedemonstratewithexperimentalevaluationthatthisapproachcansaveupto31%ofserverenergyinacluster.Ourevaluationisconductedontypicalrackmountserversinadatacentretestbedandusesarecentreal-worldworkloadtracefromtheBBCiPlayer,anextremelypopularvideoondemandserviceintheUK.

ADistributedAccessControlSystemforCloudFederations ShorouqAlansari(UniversityofSouthampton),FedericaPaci(UniversityofSouthampton),VladimiroSassone(UniversityofSouthampton)

Cloudfederationsareanewcollaborationparadigmwhereorganizationssharedataacrosstheirprivatecloudinfrastructures.However,theadoptionofcloudfederationsishinderedbyfederatedorganizations’concernsonpotentialrisksofdataleakageanddatamisuse.Forcloudfederationstobeviable,federatedorganizations’privacyconcernsshouldbealleviatedbyprovidingmechanismsthatalloworganizationstocontrolwhichusersfromotherfederatedorganizationscanaccesswhichdata.Weproposeanovelidentityandaccessmanagementsystemforcloudfederations.Thesystemallowsfederatedorganizationstoenforceattribute-basedaccesscontrolpoliciesontheirdatainaprivacy-preservingfashion.Usersaregrantedaccesstofederateddatawhentheiridentityattributesmatchthepolicies,butwithoutrevealingtheirattributestothefederatedorganizationowningdata.ThesystemalsoguaranteestheintegrityofthepolicyevaluationprocessbyusingblockchaintechnologyandIntelSGXtrustedhardware.Itusesblockchaintoensurethatusersidentityattributesandaccesscontrolpoliciescannotbemodifiedbyamalicioususer,whileIntelSGXprotectstheintegrityandconfidentialityofthepolicyenforcementprocess.Wepresenttheaccesscontrolprotocol,thesystemarchitectureanddiscussfutureextensions.

Voyager:CompleteContainerStateMigration ShripadNadgowda(IBMTJWatsonResearchCenter),SahilSuneja(IBMTJWatsonResearchCenter),NiltonBila(IBMTJWatsonResearchCenter),CanturkIsci(IBMTJWatsonResearchCenter)

Duetothesmallmemoryfootprintandfaststartuptimesofferredbycontainervirtualization,madeevermorepopularbytheDockerplatform,containersareseeingrapidadoptionasafoundationalcapabilitytobuildPaaSandSaaSclouds.Forsuchcontainerclouds,whicharefundamentallydifferentfromVMclouds,variouscloudmanagementservicesneedtoberevisited.Inthispaper,wepresentourVoyager-just-in-timelivecontainermigrationservice,designedinaccordancewiththeOpenContainerInitiative(OCI)principles.Voyagerisanovelfilesystem-agnosticandvendor-agnosticmigrationservicethatprovidesconsistentfull-systemmigration.VoyagercombinesCRIU-basedmemorymigrationtogetherwiththedatafederationcapabilitiesofunionmountstominimizemigrationdowntime.Withaunionviewofdatabetweenthesourceandtargethosts,Voyagercontainerscanresumeoperationinstantlyonthetargethost,whileperformingdiskstatetransferlazilyinthebackground.

Keddah:CapturingHadoopNetworkBehaviour JieDeng(QueenMaryUniversityLondon),GarethTyson(QueenMary),FélixCuadrado(QueenMaryUniversityofLondon),SteveUhlig(QueenMaryUniversityofLondon)

Asadistributedsystem,Hadoopheavilyreliesonthenetworktocompletedataprocessingjobs.WhileHadooptrafficisperceivedtobecriticalforjobexecutionperformance,theactualbehaviourofHadoopnetworktrafficisstillpoorlyunderstood.ThislackofunderstandinggreatlycomplicatesresearchrelyingonHadoopworkloads.Inthispaper,weexploreHadooptrafficthroughexperimentation.WeanalysethegeneratedtrafficofmultipletypesofMapReducejobs,withvaryinginputsizes,andclusterconfigurationparameters.Asaresult,wepresentKeddah,atoolchainforcapturing,modellingandreproducingHadooptraffic,forusewithnetworksimulators.KeddahcanbeusedtocreateempiricalHadooptrafficmodels,enablingreproducibleHadoopresearchinmorerealisticscenarios.

AScalableandDistributedApproachforNFVServiceChainCostMinimization ZijunZhang(UniversityofCalgary),ZongpengLi(UniversityofCalgary),ChuanWu(UniversityofHongKong),ChuanheHuang(WuhanUniversity)

Networkfunctionvirtualization(NFV)representsthelatesttechnologyadvancementinnetworkserviceprovisioning.Traditionalhardwaremiddleboxesarereplacedbysoftwareprogramsrunningonindustrystandardserversandvirtualmachines,forserviceagility,flexibility,andcostreduction.NFVusersareprovisionedwithservicechainscomposedofvirtualnetworkfunctions(VNFs).AfundamentalprobleminNFVservicechainprovisioningistosatisfyuserdemandswithminimumsystem-widecost.Wejointlyconsidertwotypesofcostinthiswork:nodalresourcecostandlinkdelaycost,andformulatetheservicechainprovisioningproblemusingnonlinearoptimization.Throughthemethodofauxiliaryvariables,wetransformtheoptimizationproblemintoitsseparableform,andthenapplythealternatingdirectionmethodofmultipliers(ADMM)todesignscalableandfullydistributedsolutions.Throughsimulationstudies,weverifytheconvergenceandefficacyofourdistributedalgorithmdesign.

ElasticPaxos:ADynamicAtomicMulticastProtocol SamuelBenz(UniversitàdellaSvizzeraitaliana),FernandoPedone(UniversitàdellaSvizzeraitaliana)

Replicationisacommontechniqueusedtodesignreliabledistributedsystemsbymaskingdefectivecomponents.TocopewiththerequirementsofmodernInternetapplications,replicationprotocolsmustallowforthroughputscalabilityanddynamicreconfiguration,thatis,on-demandreplacementorprovisioningofsystemresources.ThispaperdescribesElasticPaxos,anewdynamicatomicmulticastprotocolthatfulfillstheserequirements.ElasticPaxosallowstodynamicallyaddandremoveresourcestoanonlinepartiallyreplicatedstatemachine.WeimplementedElasticPaxosandevaluateditsperformanceinOpenStack,acloudenvironment.Wedemonstrateitspracticalitytodynamicallyscaleupanddownapartiallyreplicateddatastorewithitandtoreconfigureadistributedsystem.

BoostingTheBenefitsOfHybridSDN WenWang(McGillUniversity),WenboHe(McMasterUniversity),JinshuSu(NationalUniversityofDefenseTechnology)

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Theupgradeofalegacynetworktoafullsoftware-definednetworking(SDN)deploymentisusuallyanincrementalprocess,duringwhichSDNswitchesandlegacyswitchescoexistinthehybridnetwork.However,withinappropriatedeploymentofSDNswitchesanddesignofhybridcontrol,theadvantagesofSDNcontrolcouldnotexert,anditevenresultsinperformancedegradationorinconsistency(e.g.,loops,black-holes).Therefore,thehybridSDNrequiresconsiderablecoordinationofthecentralizedcontrolanddistributedrouting.Inthispaper,weproposeasolutiontohandletheheterogeneitycausedbydistinctforwardingcharacteristicsofSDNandlegacyswitches,thereforeboostingthebenefitsofhybridSDN.WeplanSDNplacementtoenhancetheSDNcontrollabilityoverthehybridnetwork,andconducttrafficengineeringconsideringboththeforwardingcharacteristicsofSDNandlegacyswitches.TheexperimentswithvarioustopologiesshowthattheSDNplacementplanningandhybridforwardingyieldbetternetworkperformanceespeciallyintheearly70%SDNdeployment.

AdoptingSDNSwitchBuffer:BenefitsAnalysisandMechanismDesign FuliangLi(NortheasternUniversity),JiannongCao(TheHongKongPolytechnicUniversity),XingweiWang(NortheasternUniversity),YinchuSun(NortheasternUniversity),TianPan(BeijingUniversityofPostsandTelecommunication),XuefengLiu(TheHongKongPolytechnicUniversity)

OnecriticalissueinSDNistoreducethecommunicationoverheadbetweentheswitchesandthecontroller.Suchoverheadismainlycausedbyhandlingmiss-matchpackets,becauseforeachmiss-matchpacket,aswitchwillsendarequesttothecontrolleraskingforforwardingrule.Existingapproachestoaddressthisproblemgenerallyneedtodeployintermediateproxyorauthorityswitchestoholdrulecopies,soastoreducethenumberofrequestssenttothecontroller.Inthispaper,wearguethatusingtheintrinsicbufferinaSDNswitchcanalsogreatlyreducethecommunicationoverheadwithoutusingadditionaldevices.Ifaswitchbufferseachmiss-matchpacket,onlyafewheaderfieldsinsteadoftheentirepacketarerequiredtobesenttothecontroller.Experimentresultsshowthatthiscanreduce78.7%controltrafficand37%controlleroverheadatthecostofincreasingonly5.6%switchoverheadonaverage.Iftheproposedflow-granularitybuffermechanismisadopted,onlyonerequestmessageneedstobesenttothecontrollerforanewflowwithmanyarrivalpackets.Thusthecontroltrafficandcontrolleroverheadcanbefurtherreducedby64%and35.7%respectivelyonaveragewithoutincreasingtheswitchoverhead.

ShortPaper3:InternetofThings,SmartCities,andCyber-PhysicalSystemsIOTSENTINEL:AutomatedDevice-TypeIdentificationforSecurityEnforcementinIoT MarkusMiettinen(TechnischeUniversitatDarmstadt),SamuelMarchal(AaltoUniversity),IbbadHafeez(UniversityofHelsinki),N.Asokan(AaltoUniversity),Ahmad-RezaSadeghi(TechnischeUniversitatDarmstadt),SasuTarkoma(UniversityofHelsinki)

WiththerapidgrowthoftheInternet-of-Things(IoT),concernsaboutthesecurityofIoTdeviceshavebecomeprominent.SeveralvendorsareproducingIP-connecteddevicesforhomeandsmallofficenetworksthatoftensufferfromflawedsecuritydesignsandimplementations.Theyalsotendtolackmechanismsforfirmwareupdatesorpatchesthatcanhelpeliminatesecurityvulnerabilities.Securingnetworkswherethepresenceofsuchvulnerabledevicesisgiven,requiresabrownfieldapproach:applyingnecessaryprotectionmeasureswithinthenetworksothatpotentiallyvulnerabledevicescancoexistwithoutendangeringthesecurityofotherdevicesinthesamenetwork.Inthispaper,wepresentIoTSentinel,asystemcapableofautomaticallyidentifyingthetypesofdevicesbeingconnectedtoanIoTnetworkandenablingenforcementofrulesforconstrainingthecommunicationsofvulnerabledevicessoastominimizedamageresultingfromtheircompromise.WeshowthatIoTSentineliseffectiveinidentifyingdevicetypesandhasminimalperformanceoverhead.

EfficientZ-orderEncodingBasedMulti-modelDataCompressioninWSNs XiaofeiCao(MissouriUniversityofScienceandTechnology),SanjayMadria(MissouriUniversityofScienceandTechnology),TakahiroHara(OsakaUniversity)

Wirelesssensornetworkshavesignificantlimitationsinavailablebandwidthandenergy.Thelimitedbandwidthinsensornetworkscancausehighermessagedeliverylatencyinapplicationssuchasmonitoringpoisonousgasleak.Insuchapplications,therearemulti-modalsensorswhosevaluessuchastemperature,gasconcentration,locationandCO2levelneedtobetransmittedtogetherforfasterdetectionandtimelyassessmentofgasleak.Inthispaper,weproposenovelZ-orderbaseddatacompressionschemes(Z-compression)toreduceenergyandsavebandwidthwithoutincreasingthemessagedeliverylatency.InsteadofusingthepopularHuffmantreestylebasedencoding,ZcompressionusesZ-orderencodingtomapthemultidimensionalsensingdataintoone-dimensionalbinarystreamtransmittedusingasinglepacket.Ourexperimentalevaluationsusingreal-worlddatasetsshowthatZ-compressionhasamuchbettercompressionratio,energysaving,streamingratethanknownschemeslikeLEC(andadaptiveLEC),FELACSandTinyPackformulti-modalsensordata.

PTrack:EnhancingtheApplicabilityofPedestrianTrackingwithWearables YonghangJiang(CityUniversityofHongKong),ZhenjiangLi(CityUniversityofHongKong),JianpingWang(CityUniversityofHongKong)

Theabilitytoaccuratelytrackpedestriansisvaluableforvariantapplicationdesigns.Althoughpedestriantrackinghasbeeninvestigatedexcessivelyandownedawell-suitedsensingplatform,theproposedsolutionsarefarfrombeingmatureyet.Pedestriantrackingcontainsstepcountingandstrideestimationtwocomponents.Stepcountingalreadyhascommercialproducts,buttheperformanceisstillunreliableandlesstrustworthyinpractice.Strideestimationevenstaysintheresearchstagewithoutreadysolutionsreleasedonthemarket.Suchanon-negligiblegapbetweenlong-termresearchinvestigationandtechnique'sactualusageexistsduetoaseriesofcrucialapplicabilityissuesunsolved,includingdesignvulnerabilitytointerferingactivities,extractingpurelybody'smovementfromadditivesensorsignals,andparametertrainingwithoutuser'sintervention.Inthispaper,wedeeplyanalyzehuman'sgaitcyclesandobtaininspiringobservationstoaddresstheseissues.Weincorporateourtechniquesintoexistingpedestriantrackingdesignsandimplementaprototype,PTrack,onLGsmartwatch.WefindPTrackeffectivelyenhancesthesystemapplicabilityandachievespromisingperformanceunderverypracticalsettings.

SourceLocationPrivacy-AwareDataAggregationSchedulingforWirelessSensorNetworks JackKirton(UniversityofWarwick),MatthewBradbury(TheUniversityofWarwick),ArshadJhumka(UniversityofWarwick)

Sourcelocationprivacy(SLP)isanimportantpropertyfortheclassofassetmonitoringproblemsinwirelesssensornetworks(WSNs).SLPaimstopreventanattackerfromfindingavaluableassetwhenaWSNnodeisbroadcastinginformationduetothedetectionoftheasset.MostSLPtechniquesfocusattheroutinglevel,withtypicallyhighmessageoverhead.TheobjectiveofthispaperistoinvestigatethenovelproblemofdevelopingaTDMAMACschedulethatcanprovideSLP.Wemakeanumberofimportantcontributions:(i)wedevelopanovelformalisationofaclassofeavesdroppingattackersandprovidenovelformalisationsofSLP-awaredataaggregationschedules(DAS),(ii)wepresentadecisionproceduretoverifywhetheraDASscheduleisSLP-aware,thatreturnsacounterexampleifthescheduleisnot,similartomodelchecking,and(iii)wedevelopa3-stagedistributedalgorithmthattransformsaninitialDASalgorithmintoacorrespondingSLP-awarescheduleagainstaspecificclassofeavesdroppers.OursimulationresultsshowthattheresultingSLP-awareDASprotocolreducesthecaptureratioby50%attheexpenseofnegligablemessageoverhead.

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VelocityOptimizationofPureElectricVehicleswithTrafficDynamicsConsideration LiuwangKang(UniversityofVirginia),HaiyingShen(UniversityofVirginia),AnkurSarker(UniversityofVirginia)

AsElectricVehicles(EVs)becomeincreasinglypopular,theirbattery-relatedproblems(e.g.,shortdrivingrangeandheavybatteryweight)mustberesolvedassoonaspossible.VelocityoptimizationofEVstominimizeenergyconsumptionindrivingisaneffectivealternativetohandletheseproblems.However,previousvelocityoptimizationmethodsassumethatvehicleswillpassthroughtrafficlightsimmediatelyatgreentrafficsignals.Actually,avehiclemaystillexperienceadelaytopassagreentrafficlightduetoavehiclewaitingqueueinfrontofthetrafficlight.Inthispaper,forthefirsttime,weproposeavelocityoptimizationsystemwhichenablesEVstoimmediatelypassgreentrafficlightswithoutdelay.Wecollectedrealdrivingdataona4.0kmlongroadsectionofUS-25highwaytoconductextensivetrace-drivensimulationstudies.TheexperimentalresultsfromMatlabandSimulationforUrbanMObility(SUMO)trafficsimulatorshowthatourvelocityoptimizationsystemreducesenergyconsumptionbyupto17.5%comparedwithrealdrivingpatternswithoutincreasingtriptime.

PIANO:Proximity-basedUserAuthenticationonVoice-PoweredInternet-of-ThingsDevices NeilZhenqiangGong(IowaStateUniversity),AltayOzen(IowaStateUniversity),YuWu(UCDavis),XiaoyuCao(IowaStateUniversity),RichardShin(UCBerkeley),DawnSong(UCBerkeley),HongxiaJin(SamsungResearchAmerica),XuanBao(GoogleInc.)

VoiceisenvisionedtobeapopularwayforhumanstointeractwithInternet-of-Things(IoT)devices.Weproposeaproximity-baseduserauthenticationmethod(calledPIANO)foraccesscontrolonsuchvoice-poweredIoTdevices.PIANOleveragesthebuilt-inspeaker,microphone,andBluetooththatvoice-poweredIoTdevicesoftenalreadyhave.Specifically,weassumethatausercarriesapersonalvoice-powereddevice(e.g.,smartphone,smartwatch,orsmartglass),whichservesastheuser’sidentity.Whenanothervoice-poweredIoTdeviceoftheuserrequiresauthentication,PIANOestimatesthedistancebetweenthetwodevicesbyplayinganddetectingcertainacousticsignals;PIANOgrantsaccessiftheestimateddistanceisnolargerthanauser-selectedthreshold.Weimplementedaproof-of-conceptprototypeofPIANO.Throughtheoreticalandempiricalevaluations,wefindthatPIANOissecure,reliable,personalizable,andefficient.

CategoryInformationCollectioninRFIDSystems JiaLiu(NanjingUniversity),ShigangChen(UniversityofFlorida),BinXiao(TheHongKongPolytechnicUniversity),YanyanWang(NanjingUniversity),LijunChen(NanjingUniversity)

InRFID-enabledapplications,whenatagisputintouseandassociatedwithaspecificobject,thecategory-relatedinformation(e.g.,thebrandsofclothes)aboutthisobjectmightbepreloadedintothetag’smemoryasrequired.Sincesuchinformationreflectsthecategoryattributes,alltagsinthesamecategorycarrytheidenticalcategoryinformation.Tocollectthisinformation,wedonotneedtorepeatedlyinterrogateeachtag;onetag’sresponseinacategoryissufficient.Inthispaper,weinvestigatethenewproblemofcategoryinformationcollectioninamulti-categoryRFIDsystem,whichisreferredtoasinformationsampling.Weproposeanefficienttwo-phasesamplingprotocol(TPS).Byquicklyzoomingintoacategoryandisolatingatagfromthiscategory,TPSisabletosampleacategorybybroadcastingonly7.5-bitpollingvector(veryefficientwhencomparedtothe96-bittagID).Wetheoreticallyanalyzetheprotocolperformanceanddiscusstheoptimalparametersettingsthatminimizetheoverallexecutiontime.ExtensivesimulationsshowthatTPSoutperformsthebenchmark,greatlyimprovingthesamplingperformance.

ScalableRole-basedDataDisclosureControlfortheInternetofThings AliYavari(RMITUniversity),ArezouSoltaniPanah(RMITUniversity),DimitriosGeorgakopoulos(SwinburneUniversityofTechnology),PremPrakashJayaraman(SwinburneUniversityofTechnology),RonvanSchyndel(RMITUniversity)

TheInternetofThings(IoT)isthelatestInternetevolutionthatinterconnectsbillionsofdevices,suchascameras,sensors,RFIDs,smartphones,wearabledevices,ODBIIdongles,etc.FederationsofsuchIoTdevices(or\textit{things})providestheinformationneededtosolvemanyimportantproblemsthathavebeentoodifficulttoharnessbefore.Despitethesegreatbenefits,privacyinIoTremainsagreatconcern,inparticularwhenthenumberofthingsincreases.Thispressestheneedforthedevelopmentofhighlyscalableandcomputationallyefficientmechanismstopreventunauthorisedaccessanddisclosureofsensitiveinformationgeneratedbythings.Inthispaper,weaddressthisneedbyproposingalightweight,yethighlyscalable,dataobfuscationtechnique.Forthispurpose,adigitalwatermarkingtechniqueisusedtocontrolperturbationofsensitivedatathatenableslegitimateuserstode-obfuscateperturbeddata.Toenhancethescalabilityofoursolution,wealsointroduceacontextualisationservicethatachievereal-timeaggregationandfilteringofIoTdataforlargenumberofdesignatedusers.We,then,assesstheeffectivenessoftheproposedtechniquebyconsideringahealth-carescenariothatinvolvesdatastreamedfromvariouswearableandstationarysensorscapturinghealthdata,suchasheart-rateandbloodpressure.Ananalysisoftheexperimentalresultsthatillustratetheunconstrainedscalabilityofourtechniqueconcludesthepaper.

Multi-representationbasedDataProcessingArchitectureforIoTApplications VaibhavArora(UniversityofCalifornia,SantaBarbara),FaisalNawab(UniversityofCalifornia,SantaBarbara),DivyakantAgrawal(UniversityofCalifornia,SantaBarbara),AmrElAbbadi(UniversityofCalifornia,SantaBarbara)

InternetofThings(IoT)applicationslikesmartcars,smartcitiesandwearablesarebecomingwidespreadandarethefutureoftheInternet.OneofthemajorchallengesforIoTapplicationsisefficientlyprocessing,storingandanalyzingthecontinuousstreamofincomingdatafromalargenumberofconnectedsensors.Weproposeamulti-representationbaseddataprocessingarchitectureforIoTapplications.Thedataisstoredinmultiplerepresentations,likerows,columns,graphswhichprovidessupportfordiverseapplicationdemands.Aunifyingupdatemechanismbasedondeterministicschedulingisusedtoupdatethedatarepresentations,whichcompletelyremovestheneedfordatatransferpipelineslikeETL(Extract,TransformandLoad).Thecombinationofmultiplerepresentations,andthedeterministicupdatemechanism,providestheabilitytosupportreal-timeanalyticsandcaterstoIoTapplicationsbyminimizingthelatencyofoperationslikecomputingpre-definedaggregates.

LongTermSensingviaBatteryHealthAdaptation GregJackson(ImperialCollegeLondon),ZhijinQin(ImperialCollegeLondon),JulieAMcCann(ImperialCollegeLondon)

EnergyNeutralOperation(ENO)hascreatedtheabilitytocontinuouslyoperatewirelesssensornetworksinareassuchasenvironmentalmonitoring,hazarddetectionandindustrialIoTapplications.CurrentENOapproachesutilisetechniquessuchassampleratecontrol,adaptivedutycyclinganddatareductionmethodstobalanceenergygeneration,storageandconsumption.However,thestateoftheartapproachesmakesastrongandunrealisticassumptionthatbatterycapacityisfixedthroughoutthedeploymenttimeofanapplication.ThisresultsinscenarioswhereENOsystemsoverallocatesensingtasks,thereforeasbatterycapacity

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degradesitcausesthesystemtonolongerbeenergyneutralandthenfailunexpectedly.Inthispaper,weformulatetheproblemtomaximisethequality-of-serviceintermsofdutycycleandthebatterycapacitytoextendthedeploymentlifetimeofasensingapplication.Inaddition,wedevelopalightweightalgorithmtosolvetheformulatedproblem.Moreover,weevaluatetheproposedmethodusingrealsensorenergyconsumptiondatacapturedfrommicro-climatesensorsdeployedinQueenElizabethOlympicPark,London.Resultsshowthata307%extensionofdeploymentlifetimecanbeachievedwhencomparedtoatraditionalENOsolutionwithoutareductioninthedutycycleofthesensor.

DetectingTimeSynchronizationAttacksinCyber-PhysicalSystemswithMachineLearningTechniques JingxuanWang(TheUniversityofHongKong),WentingTu(ShanghaiUniversityofFinanceandEconomics),LucasC.K.Hui(TheUniversityofHongKong),SiuMingYiu(TheUniversityofHongKong),EricKeWang(HarbinInstituteofTechnologyShenzhenGraduateSchool)

Recently,researchersfoundanewtypeofattacks,calledtimesynchronizationattack(TSattack),incyber-physicalsystems.Insteadofmodifyingthemeasurementsfromthesystem,thisattackonlychangesthetimestampsofthemeasurements.Studiesshowthattheseattacksarerealisticandpractical.However,existingdetectiontechniques,e.g.baddatadetection(BDD)andmachinelearningmethods,maynotbeabletocatchtheseattacks.Inthispaper,wedevelopa“firstdifferenceaware”machinelearning(FDML)classifiertodetectthisattack.Thekeyconceptbehindourclassifieristousethefeatureof“firstdifference”,borrowedfromeconomicsandstatistics.SimulationsonIEEE14-bussystemwithrealdatafromNYISOhaveshownthatourFDMLclassifiercaneffectivelydetectbothTSattacksandothercyberattacks.

Speed-basedLocationTrackinginUsage-basedAutomotiveInsurance LuZhou(ShanghaiJiaoTongUniversity),QingrongChen(ShanghaiJiaoTongUniversity),ZutianLuo(ShanghaiJiaoTongUniversity),HaojinZhu(ShanghaiJiaoTongUniversity),CailianChen(ShanghaiJiaoTongUniversity)

Usage-basedInsurance(UBI)isregardedasapromisingwaytooffermoreaccurateinsurancepremiumbyprofilingdrivingbehaviors.Comparedwithtraditionalinsurancewhichconsidersdrivers’historyofaccidents,trafficviolationsandetc,UBIfocusesondrivingdataandcangiveamorereasonableinsurancepremiumbasedonthecurrentdrivingbehaviors.Insurersusesensorsinsmartphoneorvehicletocollectdrivingdata(e.g.mileage,speed,harkbraking)andcomputeariskscorebasedonthesedatatorecalculateinsurancepremium.Manyinsuranceprograms,whichareadvertisedasbeingprivacy-preserving,donotdirectlyusetheGPS-basedtracking,butitisnotenoughtoprotectdriver’slocationprivacy.Inrealworld,manyenvironmentfactorssuchasreal-timetrafficandtrafficregulationscaninfluencedrivingspeed.Thesefactorsprovidetheside-channelinformationaboutthedrivingroute,whichcanbeexploitedtoinferthevehicle’strace.Basedontheobservation,weproposeanovelspeedbasedtrajectoryinferencealgorithmwhichcantrackdriversonlywiththespeeddataandoriginallocation.WeimplementtheattackonapublicdatasetinNewJersey.Theevaluationresultsshowthattheattackercanrecovertheroutewithahighsuccessfulrate.

ShortPaper4:Mobile,Wireless,Edge,andCrowdComputingOnefficientoffloadingcontrolincloudradioaccessnetworkwithmobileedgecomputing TongLi(TsinghuaUniversity),ChathuraSarathchandraMagurawalage(UniversityofEssex),KezhiWang(UniversityofEssex),KeXu(TsinghuaUniversity),KunYang(UniversityofEssex),HaiyangWang(UniversityofMinnesotaatDuluth)

Cloudradioaccessnetwork(C-RAN)andmobileedgecomputing(MEC)haveemergedaspromisingcandidatesforthenextgenerationaccessnetworktechniques.Unfortunately,althoughMECtriestoutilizethehighlydistributedcomputingresourcesincloseproximitytouserequipmentsequipments(UE),C-RANsuggeststocentralizethebasebandprocessingunits(BBU)deployedinradioaccessnetworks.Tobetterunderstandandaddresssuchaconflict,thispapercloselyinvestigatestheMECtaskoffloadingcontrolinC-RANenvironments.Inparticular,wefocusonperspectiveofmatchingproblem.OurmodelsmartlycapturestheuniquefeaturesinbothMECandC-RANwithrespecttocommunicationandcomputationefficiencyconstraints.Wedividethecross-layeroptimizationintothefollowingthreestages:(1)matchingbetweenremoteradioheads(RRH)andUEs,(2)matchingbetweenBBUsandUEs,and(3)matchingbetweenmobileclones(MC)andUEs.ByapplyingtheGale-ShapleyMatchingTheoryintheduplexmatchingframework,weproposeamulti-stageheuristictominimizetherefusalrateforuser'staskoffloadingrequests.Trace-basedsimulationconfirmsthatoursolutioncansuccessfullyachievenear-optimalperformanceinsuchahybriddeployment.

LocationPrivacyinMobileEdgeClouds TingHe(PennsylvaniaStateUniversity),ErtugrulCiftcioglu(ArmyResearchLaboratory),ShiqiangWang(IBM),KevinChan(ArmyResearchLaboratory)

Inthispaper,weconsideruserlocationprivacyinmobileedgeclouds(MECs).MECsaresmallcloudsdeployedatthenetworkedgetooffercloudservicesclosetomobileusers,andmanysolutionshavebeenproposedtomaximizeservicelocalitybymigratingservicestofollowtheirusers.Co-locationofauserandhisservice,however,impliesthatacybereavesdropperobservingservicemigrationsbetweenMECscanlocalizetheuseruptooneMECcoveragearea,whichcanbefairlysmall(e.g.,afemtocell).Weconsiderusingchaffservicestodefendagainstsuchaneavesdropper,withfocusonstrategiestocontrolthechaffs.Assumingtheeavesdropperperformsmaximumlikelihood(ML)detection,weconsiderbothheuristicstrategiesthatmimictheuser'smobilityandoptimizedstrategiesdesignedtominimizethedetectionortrackingaccuracy.Weshowthatasinglechaffcontrolledbytheoptimalstrategycandrivetheeavesdropper'strackingaccuracytozerowhentheuser'smobilityissufficientlyrandom.Theefficacyofoursolutionsisverifiedthroughextensivesimulations.

ApproximationDesignforCooperativeRelayDeploymentinWirelessNetworks HaotianWang(ShanghaiJiaoTongUniversity),ShileiTian(ShanghaiJiaoTongUniversity),XiaofengGao(ShanghaiJiaoTongUniversity),LidongWu(UniversityofTexasatTyler),GuihaiChen(ShanghaiJiaoTongUniversity)

Inthispaper,weaimtomaximizeusers’satisfactionbydeployinglimitednumberofrelaysinatargetregiontoformawirelessrelaynetwork,anddefinetheDeploymentofCooperativeRelay(DoCR)problem,whichisprovedtobeNP-complete.WefirstproposeanO(δlogn)approximationalgorithmthatutilizesthealgorithmsforbudgetweightedSteinertreeproblemwithnovelpositionweightingassignment.WefurtherproposeaheuristicmethodtosolvetheDoCRproblemreleasingpotentiallocationconstraint.Ourextensiveexperimentsindicatethatthealgorithmsweproposecansignificantlyimprovethetotalsatisfactionofthenetwork.Furthermore,weestablishatestbedusingUSRPtoshowcaseourdesignsinrealscenarios.Tothebestofourknowledge,wearethefirsttoproposeapproximationalgorithmforrelayplacementproblemtomaximizeusersatisfaction,whichhasboththeoreticalandpracticalsignificanceintherelatedarea.

DispersingSocialContentinMobileCrowdthroughOpportunisticContacts LeiZhang(SimonFraserUniversity),FengWang(TheUniversityofMississippi),JiangchuanLiu(SouthChinaAgriculturalUniversity)

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Crowdsourcedcontentsharinghasbecomeafast-growingactivityintoday'sonlinesocialnetworks,wherecontentsofinterestarecreatedbydiversesourceusersandconveyedoverthenetworkasfriendsviewandreshare.Therapidandboundlesspropagationinamobilecrowdhoweveroftencreatesseverebottlenecksontheserversideandincurssignificantenergyandmonetarycostsonthemobileside,particularlywiththestillexpensive3G/4Gcellularconnections.ThispaperpresentsSoCrowd,anovelframeworkforlarge-scalecontentsharinginamobilecrowdbyexploitingcontacts,i.e.,usershappentomoveclosewithsuchshortrangelowpowercommunicationsasWiFiandbluetoothbeingenabled.Weformulatetheschedulingproblemforsocialcontentpropagationinamobilecrowdwithcontacts,andpresentoptimaldynamicprogrammingsolution,whichfurthermotivatesaseriesofpracticalheuristics.TheeffectivenessofSoCrowdhasbeendemonstratedbyextensivesimulationsdrivenbyrealworldtracesanddatasets.

ALightweightRecommendationFrameworkforMobileUser'sLinkSelectioninDenseNetwork JiWang(NationalUniversityofDefenseTechnology),XiaominZhu(NationalUniversityofDefenseTechnology),WeidongBao(NationalUniversityofDefenseTechnology),GuanlinWu(NationalUniversityofDefenseTechnology)

Withtheproliferationofmobiledevicesandthedevelopmentofcommunicationtechnology,mobiledeviceshavepermeatedeveryaspectofourdailylives.However,indensenetworkwherelargecrowdofmobiledevicestrytoaccesstothenetworksimultaneously,thesevereinterferencebetweenmobiledevicesmayincuraremarkabledeteriorationofthewirelesscommunicationquality.Howtoimproveindividual'sexperienceinsuchscenarioisacriticalyetopenproblem.Inspiredbythemobiledeviceusers'usagepatternaswellasthecharacteristicofmostwirelesscommunicationsystems,weproposeaframeworkofferinguplink/downlinkselectionrecommendationtodifferentmobiledeviceuserstoenhancetheirutilityinthispaper.Thedesignoftheframeworkstartswithformulatingtheproblemasalinkselectiongame.AnalysisshowsthatthegamecanbecategorizedasageneralizedordinalpotentialgamewhoseNashEquilibriumisguaranteed.WethendeviseadistributedlinkselectionalgorithmtogenerateaNashEquilibriumofthegame.Toaccommodatetothecharacteristicofdensenetworkandthecapacitylimitationofmobiledevice,thedesignofthealgorithmshowsalight-weightpropertyanddoesnotrequireeachmobiledeviceusertoknowothers'currentselection.Theprobabilityofincompleteinformationgatheringisalsoconsidered.Extensiveexperimentsareconductedtodemonstratetheeffectivenessandsuperiorityoftheproposedframework.Experimentalresultsshowthattheglobalaverageutilityincreaseratereachesabove20%,andabout70%mobiledeviceuserscanbenefitfromusingourframework

MakingSmartphoneSmartonDemandforLongerBatteryLife MarcoBrocanelli(TheOhioStateUniversity),XiaoruiWang(TheOhioStateUniversity)

Amajorconcernfortoday’ssmartphonesistheirmuchfasterbatterydrainthantraditionalfeaturephones,despitetheirgreaterbatterycapacities.Thedifferenceismainlycontributedbythosemorepowerfulbutalsomuchmorepowerconsumingsmartphonecomponents,suchasthemulti-coreapplicationprocessor.Whiletheapplicationprocessormustbeactivewhenanysmartappsarebeingused,itisalsounnecessarilywakenup,evenduringidleperiods,toperformoperationsrelatedtobasicphonefunctions(i.e.,incomingcallsandtextmessages).Inthispaper,weinvestigatehowtoincreasethebatterylifeofsmartphonesbyminimizingtheuseoftheapplicationprocessorduringidleperiods.Wefindthattheapplicationprocessorisoftenwakenupbyaprocessrunningonit,calledtheRadioInterfaceLayerDaemon(RILD),whichinterfacestheuserandappstotheGSM/LTEcellularnetwork.Inparticular,wedemonstratethatagreatamountofenergycouldbesavedifRILDisstopped,suchthattheapplicationprocessorcansleepmoreoften.Basedonthiskeyfinding,wedesignaSmartOnDemand(SOD)configurationthatreducessmartphoneidleenergyconsumptionbyrunningRILDoperationsonasecondarylow-powermicrocontroller.Asaresult,RILDoperationscanbehandledatmuchlowerenergycostsandtheapplicationprocessoriswakenuponlywhenoneneedstouseanysmartapps,inanon-demandmanner.WehavebuiltahardwareprototypeofSODandevaluateditwithrealusertraces.OurresultsshowthatSODcanincreaseitsbatterylifebyupto2.5moredays.

FADEWICH:FastDeauthenticationovertheWirelessChannel GiulioLovisotto(UniversityofOxford),MauroConti(UniversityofPadua),IvanMartinovic(UniversityofOxford),GeneTsudik(UniversityofCalifornia,Irvine)

Bothauthenticationanddeauthenticationareinstrumentalforpreventingunauthorizedaccesstocomputersandotherresources.Whilethereareobviousmotivatingfactorsforusingstrongauthenticationmechanisms,convincinguserstodeauthenticateisnotstraight-forward,sincedeauthenticationisnotconsideredmandatory.Auserwholeavesalogged-inworkstationunattended(especiallyforashorttime)istypicallynotinconveniencedinanyway;infact,theotherwayaround–noannoyingreauthenticationisneededuponreturn.However,anunattendedworkstationistriviallysusceptibletothewell-known“lunchtimeattack”byanynearbyadversarywhosimplytakesoverthedeparteduser’slog-insession.Atthesametime,sincedeauthenticationdoesnotintrinsicallyrequireusersecrets,itcan,inprinciple,bemadeunobtrusive.Tothisend,thispaperdesignsthefirstautomaticuserdeauthenticationsystem–FADEWICH–thatdoesnotrelyonbiometric-orbehavior-basedtechniques(e.g.,keystrokedynamics)anddoesnotrequireuserstocarryanydevices.Itusesphysicalpropertiesofwirelesssignalsandtheeffectofhumanbodiesontheirpropagation.ToassessFADEWICH’sfeasibilityandperformance,extensiveexperimentswereconductedwithitsprototype.Resultsshowthatitsufficestohavenineinexpensivewirelesssensorsdeployedinasharedofficesettingtocorrectlydeauthenticatealluserswithinsixseconds(90%withinfourseconds)aftertheyleavetheirworkstation’svicinity.WeconsideredtworealisticscenarioswheretheadversaryattemptstosubvertFADEWICHandshowedthatlunchtimeattacksfail.

CognitiveWirelessCharger:Sensing-BasedReal-TimeFrequencyControlForNear-FieldWirelessCharging Sang-YoonChang(UniversityofColoradoColoradoSprings),SristiLakshmiSravanaKumar(AdvancedDigitalSciencesCenter),Yih-ChunHu(UniversityofIllinoisatUrbana-Champaign)

ArecentincreaseinmobileandIoTdeviceshasledtotheadvancementofwirelesscharging.Thestate-of-the-artwirelesschargingsystemsoperateataparticularfrequency,controlledbytheexplicitnetworkingfromthepower-receivingdevice(whichrelaysthebatterystatusinformation,usefulforthefrequencyselection),butsuchcontrolisnotdesignedtocopewiththevariationsinthepowerreceivingdevice’splacementsandalignments(whicharemoresignificantinnear-fieldandpseudo-tightlycoupledchargingapplications,asmorechargingpadsarebeingdeployedinthepublicdomainsandservingheterogeneousclients).Inthiswork,weanalyzetheimpactofthepowertransferperformancecausedbythepowerreceiver’sload,distance,andcoilalignment/overlapandintroducecognitivewirelesscharger(CWC),whichadaptivelycontrolstheoperatingfrequencyinreal-timeusingimplicitfeedbackfromsensingforoptimaloperations.InadditiontothetheoreticalandLTSpice-basedsimulationanalysis,webuildaprototypecompatibletotheQistandardandanalyzetheperformanceofCWCwithit.Throughouranalyses,weestablishthatfrequencycontrolachievesperformancegainsininductive-couplingchargingapplicationsandissensitivetothevariationsintheplacementandalignmentbetweenthepower-transmittingandthepower-receivingcoils.Ourprototype,whenCWCisturnedoff,hascomparableperformancetothecommercial-gradeQiwirelesschargersand,withCWCenabled,demonstratessignificantimprovementovermodernwirelesschargers.

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DensityandMobility-drivenEvaluationofBroadcastAlgorithmsforMANETs RazielCarvajalGómez(UniversityofNeuchatel),IntiGonzalez-Herrera(LaBRI/UniversityofBordeaux),Yérom-DavidBromberg(UniversityofRennes1),LaurentRéveillère(UniversityofBordeaux),EtienneRivière(UniversityofNeuchatel)

BroadcastisafundamentaloperationinMobileAd-HocNetworks(MANETs).Alargevarietyofbroadcastalgorithmshavebeenproposed.Theydifferinthewaymessageforwardingbetweennodesiscontrolled,andinthelevelofinformationaboutthetopologythatthiscontrolrequires.DeploymentscenariosforMANETsvarywidely,inparticularintermsofnodesdensityandmobility.Thechoiceofanalgorithmdependsonitsexpectedcoverageandenergycost,whicharebothimpactedbythedeploymentcontext.Inthiswork,weareinterestedinthecomprehensivecomparisonofthecostsandeffectivenessofbroadcastalgorithmsforMANETsdependingontargetenvironmentalconditions.Wedescribetheresultsofanexperimentalstudyoffivealgorithms,representativeofthemaindesignalternatives.Ourstudyrevealsthatthebestalgorithmforagivensituation,suchasahighdensityandastablenetwork,isnotnecessarilythemostappropriateforadifferentsituationsuchasasparseandmobilenetwork.Weidentifythealgorithmscharacteristicsthatarecorrelatedwiththesedifferencesanddiscusstheprosandconsofeachdesign.

Energy-AwareCPUFrequencyScalingforMobileVideoStreaming WenjieHu(ThePennsylvaniaStateUniversity),GuohongCao(ThePennsylvaniaStateUniversity)

TheenergyconsumedbyvideostreamingincludestheenergyconsumedfordatatransmissionandCPUprocessing,whicharebothaffectedbytheCPUfrequency.HighCPUfrequencycanreducethedatatransmissiontimebutitconsumesmoreCPUenergy.LowCPUfrequencyreducestheCPUenergybutincreasesthedatatransmissiontimeandthenincreasestheenergyconsumption.Inthispaper,weaimtoreducethetotalenergyofmobilevideostreamingbyadaptivelyadjustingtheCPUfrequency.Basedonrealmeasurementresults,wemodeltheeffectsofCPUfrequencyonTCPthroughputandsystempower.Basedonthesemodels,weproposeanEnergy-awareCPUFrequencyScaling(EFS)algorithmwhichselectstheCPUfrequencythatcanachieveabalancebetweensavingthedatatransmissionenergyandCPUenergy.Sincethedownloadingscheduleofexistingvideostreamingappsisnotoptimizedintermsofenergy,wealsoproposeamethodtodeterminewhenandhowmuchdatatodownload.Throughtrace-drivensimulationsandrealmeasurement,wedemonstratethattheEFSalgorithmcanreduce30%ofenergyfortheYoutubeapp,andthecombinationofourdownloadmethodandEFSalgorithmcansave50%ofenergythanthedefaultYoutubeapp.

CrazyCrowdSourcingtoMitigateResourceScarcity NovaAhmed(NorthSouthUniversity),MdMahfuzurRahmanSiddiquee(IndependentResearcher),RefayaKarim(NorthSouthUniversity),MohsinaZaman(IndependentResearcher),SayedMahmudulAlam(NorthSouthUniversity),SyedFahimAsraf(NorthSouthUniversity)

Resourcescarcityprohibitsdevelopingcountrypopulationinmanywaysfromubiquitousservices.OnesuchserviceisprovidinginformationaboutthebestrouteforanambulanceinacrisissituationduetolackofproperroadnetworkinformationandGPSdata.WehaveworkedonaroutingmethodinDhaka,Bangladeshandutilizedthepowerofcrowdsourcingintimesofresourcescarcity.Weshareourchallengesandopportunitiesthatopenedupfollowedbythechallengesinthispaper.

DetectingRogueAPwiththeCrowdWisdom TongqingZhou(NationalUniversityofDefenseTechnology),ZhipingCai(NationalUniversityofDefenseTechnology),BinXiao(TheHongKongPolytechnicUniversity),YueyueChen(NationalUniversityofDefenseTechnology),MingXu(NationalUniversityofDefenseTechnology)

WiFinetworksarevulnerabletorogueAPattacksinwhichanattackersetsupanimposterAPtoluremobileuserstoconnect.Theattackercaneavesdroponthecommunication,severelythreateningusers'privacy.ExistingrogueAPdetectionsolutionsareconfinedtosomespecificattackscenarios(e.g.,byrelayingthetraffictoatargetAP)orrequireadditionalhardware.Inthispaper,weproposeacrowdsensingbasedapproach,namedCRAD,todetectrogueAPsincamouflagewithoutspecializedhardwarerequirement.CRADexploitsthespatialcorrelationofRSStoidentifyapotentialimposter,whichshouldbeatadifferentlocationfromthelegitimateone.TheRSSmeasurementscollectedfromthecrowdfacilitatearobustprofileandminimizetheinaccuracyeffectofasingleRSSvalue.Asaresult,CRADcanfilteroutabnormalsamplessensedintherealtimebydynamicallymatchingtheprofile.Weevaluateourapproachwithbothapublicdatasetandarealprototype.TheresultsshowthatCRADcanyield90%detectionaccuracyandprecisionwithpropercrowdpresence,evenwhentherogueAPislaunchedclosetothelegitimateone(e.g.,within1m).

ShortPaper5:DistributedBigDataSystemsandAnalyticsTowardsMultilingualAutomatedClassificationSystems AibekMusaev(UniversityofAlabama),CaltonPu(GeorgiaInstituteofTechnology)

Inthispaperweproposeandevaluatethreeapproachesforautomatedclassificationoftextsinover60languageswithouttheneedforamanuallyannotateddatasetinthoselanguages.AllapproachesarebasedontherandomizedExplicitSemanticAnalysismethodusingmultilingualWikipediaarticlesastheirknowledgerepository.WeevaluatetheproposedapproachesbyclassifyingaTwitterdatasetinEnglishandPortugueseintorelevantandirrelevantitemswithrespecttolandslideasanaturaldisaster,wherethehighestachievedF1-scoreis0.93.Theseapproachescanbeusedinvariousapplicationswheremultilingualclassificationisneeded,includingmultilingualdisasterreportingusingSocialMediatoimprovecoverageandincreaseconfidence.Asillustration,wepresentademonstrationthatcombinesdatafromphysicalsensorsandsocialnetworkstodetectlandslideeventsreportedinEnglishandPortuguese.

TheJointEffectsofTweetContentSimilarityandTweetInteractionsforTopicDerivation RobertusNugroho(MacquarieUniversity),WeiliangZhao(MacquarieUniversity),JianYang(MacquarieUniversity),CecileParis(CSIRO–ICTCentre),SuryaNepal(CSIRO)

Interactionsamongtweets,i.e.,mentions,retweets,replies,areimportantfactorscontributingtothequalityoftopicderivationonTwitter.Ifappliedcorrectly,theincorporationoftweetinteractionscansignificantlyimprovethequalityoftopicderivationincomparisonwithapproachesthataremainlybasedonthecontentsimilarityanalysis.However,howinteractionscanbemeasuredandintegratedwithcontentsimilarityfortopicderivationremainsachallenge.Inpreviouswork,thestrengthoftweet-to-tweetrelationshiphasbeencomputedbysimplyaddingmeasuresforcontentsimilarity,mentions,andreply-retweets.Thissimplelinearadditiondoesnotaccuratelyreflectthevariousimpactsthesefactorshaveontweetrelationships.Inordertoaddressthisissue,weproposeajointprobabilitymodelthatcaneffectivelyintegratetheeffectsofthecontentsimilarity,mentions,andreply-retweetstomeasurethetweetrelationshipforthepurposeoftopic

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derivation.Theproposedmethodisbasedonmatrixfactorizationtechniques,whichenablesaflexibleimplementationonadistributedsysteminanincrementalmanner.Experimentalresultsshowthattheproposedmodelresultsinasignificantimprovementinthequalityoftopicderivationoverexistingmethods.

Timed-releaseofSelf-emergingDatausingDistributedHashTables ChaoLi(UniversityofPittsburgh),BalajiPalanisamy(UniversityofPittsburgh)

Releasingprivatedatatothefutureisachallengingproblem.Makingprivatedataaccessibleatafuturepointintimerequiresmechanismstokeepdatasecureandundiscoveredsothatprotecteddataisnotavailablepriortothelegitimatereleasetimeandthedataappearsautomaticallyattheexpectedreleasetime.Inthispaper,wedevelopnewmechanismstosupportself-emergingdatastoragethatsecurelyhidekeysofencrypteddatainaDistributedHashTable(DHT)networkthatmakestheencryptionkeysautomaticallyappearatthepredeterminedreleasetimesothattheprotectedencryptedprivatedatacanbedecryptedatthereleasetime.Weshowthatastraight-forwardapproachofprivatelystoringkeysinaDHTispronetoanumberofattacksthatcouldeithermakethehiddendataappearbeforetheprescribedreleasetime(release-aheadattack)ordestroythehiddendataaltogether(dropattack).Wedevelopasuiteofself-emergingkeyroutingmechanismsforsecurelystoringandroutingencryptionkeysintheDHT.Weshowthattheproposedschemeisresilienttobothrelease-aheadattackanddropattackaswellastoattacksthatariseduetotraditionalchurnissuesinDHTnetworks.Ourexperimentalevaluationdemonstratestheperformanceoftheproposedschemesintermsofattackresilienceandchurnresilience.

CachingforPatternMatchingQueriesinTimeEvolvingGraphs:ChallengesandApproaches MuhammadNisar(UniversityofGeorgia),SaharVoghoei(UniversityofGeorgia),LakshmishRamaswamy(UniversityofGeorgia)

Patternmatchingisanimportantclassofproblemsrelatedtographs.Itisafundamentalproblemformanyapplicationsandhasbeenextensivelystudiedinliterature.Withtheadventofhugegraphs,thechallengesinthisdomainhaveincreasedmanifold.Consequentlyalotofrecentresearchhasledtonewarchitecturesandapproachesforoptimizedsolutionstothepatternmatchingproblem.Avastmajorityofthesegraphshardlyremainstaticandconstantlyevolveovertime(likesocialnetworks,webgraphs,etc).Recently,cachinghasbeenstudiedinthecontextofstaticgraphstooptimizethethroughputofqueryprocessingsystems.Inthispaper,welistthechallengesincachinginthecontextofTimeEvolvingGraphs(TEGs).Amongstothers,onemajorchallengeisconsistencywhichentailstomakingsurethecacheisconsistentwiththestreamingchanges.Weproposeanapproachtosuccessfullyimplementcachingthataddressesthoseissuesandbasedontheinitialresults,weseesignificantgainsintheoverallperformanceofsystem.

GraphA:AdaptivePartitioningforNaturalGraphs DongshengLi(NationalUniversityofDefenseTechnology),ChengfeiZhang(NationalUniversityofDefenseTechnology),JinyanWang(NationalUniversityofDefenseTechnology),ZhaoningZhang(NationalUniversityofDefenseTechnology),YimingZhang(NationalUniversityofDefenseTechnology)

Large-scalegraphcomputationiscentraltoapplicationsrangingfromlanguageprocessingtosocialnetworks.However,naturalgraphstendtohaveskewedpower-lawdistributionswhereasmallsubsetoftheverticeshavealargenumberofneighbors.Existinggraph-parallelsystemssufferfromloadimbalance,highcommunicationcost,orsuboptimalandcomplexprocessing.InthispaperwepresentGraphA,anAdaptiveapproachtoefficientpartitioningandcomputationoflarge-scalenaturalgraphs.GraphAprovidesanadaptiveanduniformgraphpartitioningalgorithm,whichpartitionsthedatasetsinaload-balancedmannerbyusinganincrementalnumberofhashfunctions.WehaveimplementedGraphAbothonSparkandonGraphLab.ExtensiveevaluationshowsthatGraphAremarkablyoutperformsstate-of-the-artgraph-parallelsystems(GraphXandPowerLyra)iningresstime,executiontimeandstorageoverhead,forbothreal-worldandsyntheticgraphs.

ParallelAlgorithmforCoreMaintenanceinDynamicGraphs NaWang(HuazhongUniversityofScienceandTechnology),DongxiaoYu(HuazhongUniversityofScienceandTechnology),HaiJin(HuazhongUniversityofScienceandTechnology),ChenQian(HuazhongUniversityofScienceandTechnology),XiaXie(HuazhongUniversityofScienceandTechnology),Qiang-ShengHua(HuazhongUniversityofScienceandTechnology)

Thispaperinitiatesthestudiesofparallelalgorithmforcoremaintenanceindynamicgraphs.Thecorenumberisafundamentalindexreflectingthecohesivenessofagraph,whichiswidelyusedinlarge-scalegraphanalytics.Weinvestigatetheparallelisminthecoreupdateprocesswhenmultipleedgesandverticesareinserted.Specifically,wediscoverastructurecalledsuperioredgeset,theinsertionofedgesinwhichcanbeprocessedinparallel.Basedonthestructureofsuperioredgeset,anefficientparallelalgorithmisthendevised.Tothebestofourknowledge,theproposedalgorithmisthefirstparalleloneforthefundamentalcoremaintenanceproblem.Finally,extensiveexperimentsareconductedondifferenttypesofreal-worldandsyntheticdatasets,andtheresultsillustratetheefficiency,stabilityandscalabilityoftheproposedalgorithm.Thealgorithmshowsasignificantspeedupintheprocessingtimecomparedwithpreviousresultsthatsequentiallyhandleedgeandvertexinsertions.

DHCRF:ADistributedConditionalRandomFieldAlgorithmonHeterogeneousCPU-GPUClusterforBigData AiWei(HunanUniversity),LiKenli(HunanUniversity),ChenCen(HunanUniversity),PengJiwu(HunanUniversity),LiKeqin(HunanUniversity)

Asoneofthemostrecognizedmodelsinmachinelearning,theconditionalrandomfields(CRF)hasbeenwidelyusedinmanyapplications.AstheparameterestimationofCRFishighlytime-consuming,howtoimprovetheperformanceofCRFhasreceivedsignificantattention,inparticularinthebigdataenvironment.Todealwithlarge-scaledata,CPU-basedorGPU-basedparallelizationsolutionshavebeenproposedtoimproveperformance.However,theproblemisanongoingone.Inthispaper,wefocusonthebigdataenvironmentandproposeadistributedCRFonaheterogeneousCPU-GPUclustercalledDHCRF.Ourapproachdiffersfrompreviouswork.Specifically,itleveragesathree-stageheterogeneousMapandReduceoperationtoimprovetheperformance,makingfulluseofCPU-GPUcollaborativecomputingcapabilitiesinabigdataenvironment.Furthermore,bycombiningelasticdatapartitionandintermediateresultsmultiplexingmethod,thedistributedCRFisoptimized.Elasticdatapartitionisperformedtokeeptheloadbalanced,andtheintermediateresultsmultiplexingmethodisadoptedtoreducedatacommunication.ExperimentalresultsshowthattheDHCRFoutperformsthebaselineCRFalgorithmandtheCPU-basedparallelCRFalgorithmwithnotableperformanceimprovementwhilemaintainingcompetitivecorrectnessatthesametime.

TowardsNewAbstractionsforImplementingQuorum-basedSystems TormodErevikLea(UniversityofStavanger),LeanderJehl(UniversityofStavanger),HeinMeling(UniversityofStavanger)

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ThispaperintroducesGorums,anovelRPCframeworkforbuildingfaulttolerantdistributedsystems.Gorumsoffersaflexibleandsimplequorumcallabstraction,usedtocommunicatewithasetofprocesses,andtocollectandprocesstheirresponses.Gorumsprovidesseparateabstractionsfor(a)selectingprocessesforaquorumcalland(b)processingreplies.Theseabstractionssimplifythemaincontrolflowofprotocolimplementations,especiallyforquorum-basedsystems,whereonlyasubsetoftherepliestoaquorumcallneedtobeprocessed.ToshowthatGorumscanbeusedinpracticalsystems,weimplementedEPaxos’latency-efficientquorumsystem,andranexperimentsusingakey-valuestorage.OurresultsshowthatGorums’abstractionscanprovideadditionalperformancebenefitstoEPaxos.

SelectiveTrafficOffloadingOntheFly:aMachineLearningApproach ZaiyangTang(HuazhongUniversityofScienceandTechnology),PengLi(TheUniversityofAizu),SongGuo(TheHongKongPolytechnicUniversity),XiaofeiLiao(HuazhongUniversityofScienceandTechnology),HaiJin(HuazhongUniversityofScienceandTechnology),DaqingZhang(InstitutMines-Telecom,TelecomSudParis)

Ithasbeenwellrecognizedthatnetworktransmissionconstitutesalargeportionofsmartphoneenergyconsumption,mainlybecauseofthetailenergycausedbycellularnetworkinterface.Trafficoffloadinghasbeenproposedtoreduceenergybylettingasmartphoneoffloadnetworktraffictoitsneighborsinvicinityvialow-powerdirectconnections(e.g.,WiFiDirectorBluetooth).Ourexperimentsconductedinarealisticenvironmentrevealthatenergyefficiencycannotbeimprovedorevendeteriorateswithoutacarefullydesignedoffloadingstrategy.Inthispaper,weproposeaselectivetrafficoffloadingschemeimplementedasasmartphonemiddlewareinasoftware-definedfashion,whichconsistsofapacketclassifierandatrafficscheduler.Usingalight-weightmachinelearningapproachexploitinguniquesmartphonecontextinformation,thepacketclassifieridentifiespacketsgeneratedontheflyasoffloadableornotwithsubstantiallyimprovedefficiencyandfeasibilityonresourcelimitedsmartphonescomparedtotraditionalapproaches.Bothtestbedandsimulationbasedexperimentsareconductedandtheresultsshowthatourproposalalwaysattainsthesuperiorperformanceonanumberofcomparisonmetrics.

AFastHeuristicAttributeReductionAlgorithmusingSpark MinchengChen(WuhanUniversityofTechnology),JinglingYuan(WuhanUniversityofTechnology),LinLi(WuhanUniversityofTechnology),DonglingLiu(WuhanUniversityofTechnology),TaoLi(UniversityofFlorida)

Energydata,whichconsistsofenergyconsumptionstatisticsandotherrelateddataingreendatacenters,growsdramatically.Theenergydatahasgreatvalue,butmanyattributeswithinitareredundantandunnecessary.Thusattributereductionfortheenergydatahasbeenconceivedasacriticalstep.However,manyexistingattributereductionalgorithmsareoftencomputationallytime-consuming.Toaddresstheseissues,weextendthemethodologyofroughsetstoconstructdatacenterenergyconsumptionknowledgerepresentationsystem.Bytakinggoodadvantageofin-memorycomputing,anattributereductionalgorithmforenergydatausingSparkisproposed.Inthisalgorithm,weuseaheuristicformulaformeasuringthesignificanceofattributetoreducesearchspace,andanefficientalgorithmforsimplifyingenergyconsumptiondecisiontable,whichfurtherimprovethecomputationefficiency.Theexperimentalresultsshowthespeedofouralgorithmgainsupto0.28XperformanceimprovementoverthetraditionalattributereductionalgorithmusingSpark.

ProfilingUsersbyModelingWebTransactions RadekTomsu(AaltoUniversity),SamuelMarchal(AaltoUniversity),N.Asokan(AaltoUniversity)

Usersofelectronicdevices,e.g.,laptop,smartphone,etc.havecharacteristicbehaviorswhilesurfingtheWeb.Profilingthisbehaviorcanhelpidentifythepersonusingagivendevice.Inthispaper,weintroduceatechniquetoprofileusersbasedontheirwebtransactions.Wecomputeseveralfeaturesextractedfromasequenceofwebtransactionsandusethemwithone-classclassificationtechniquestoprofileauser.Weassesstheefficacyandspeedofourmethodatdifferentiating25syntheticusersonabenchmarkdataset(fromamajorsecurityvendor)representing6monthsofwebtrafficmonitoringfromasmallenterprisenetwork.

JeCache:Just-EnoughDataCachingwithJust-in-TimePrefetchingforBigDataApplications YifengLuo(FudanUniversity),JiaShi(FudanUniversity),ShuigengZhou(FudanUniversity)

Bigdataclustersintroduceanintermediatecachelayerbetweenthecomputingframeworksandtheunderlyingdistributedfilesystems,toenableupper-levelapplicationsorenduserstoefficientlyaccessbigdatasetsincacheandeffectivelysharethemamongdifferentcomputingframeworks.Ascachesaresharedbymultipleapplicationsorendusers,directlyapplyingexistingon-demandcachingstrategieswillresultinintenseconflicts,whenbigdatasetsarecachedasawhole.Meanwhile,bigdataapplicationsusuallyinvolvemassivenumbersoffilescans,cached-indatablocksmayhavelittlechanceofbeingaccessedbeforetheyarecachedouttomakewayforotheron-demanddatablocks.Thus,itisunwisetocachedatablockslongbeforetheyareactuallyaccessed.Inthispaper,weproposeanoveljust-enoughbigdatacachingschemeforjust-in-timeblockprefetchingtoimprovethecacheeffectivenessofbigdataclusters.Withjust-in-timeblockprefetching,ablockiscachedinjustbeforethetaskbeginstoprocesstheblock,ratherthanbeingcachedinalongwithotherblocksofthesamedatasetbeingprocessed.Wemonitorblockaccessestomeasuretheaverageprocessingtimeofdatablocks,andthenestimatetheminimalnumberofblocksthatshouldbekeptincacheforabigdataset,sothatthespeedofdataprocessingmatcheswiththatofdataprefetching,andeachupper-leveltaskcanobtainitsinputblocksfromcachejustintime.Ourexperimentalresultsshowthattheproposedcachemethodcanrestrainover-requirementofcacheresourcesinbigdataapplications,andprovidesthesameperformanceimprovementaswhenalldatablocksarecached.

ShortPaper6:Security,Privacy,Trust,andFaultToleranceinDistributedSystemsProximityAwarenessApproachtoEnhancePropagationDelayontheBitcoinPeer-to-PeerNetwork MuntadherFadhilSallal(UniversityofPortsmouth),GarethOwenson(UniversityofPortsmouth),MoAdda(UniversityofPortsmouth)

IntheBitcoinsystem,apeer-to-peerelectroniccurrencysystem,thedelayoverheadintransactionverificationpreventstheBitcoinfromgainingincreasingpopularitynowadaysasitmakesthesystemvulnerabletodoublespendattacks.Thispaperintroducesaproximity-awareextensiontothecurrentBitcoinprotocol,namedBitcoinClusteringBasedPingTimeprotocol(BCBPT).Theultimatepurposeoftheproposedprotocol,thatisbasedonhowtheclustersareformulatedandthenodesdefinetheirmembership,istoimprovethetransactionpropagationdelayintheBitcoinnetwork.InBCBPT,theproximityofconnectivityintheBitcoinnetworkisincreasedbygroupingBitcoinnodesbasedonpinglatenciesbetweennodes.Weshow,throughsimulations,thattheproximitybasepinglatencydefinesbetterclusteringstructuresthatoptimizetheperformanceofthetransactionpropagationdelay.Thereductionofthecommunicationlinkcostmeasuredbytheinformationpropagationtimebetweennodesismainlyconsideredasakeyreasonforthisimprovement.BitcoinClusteringBasedPingTimeprotocolismoreeffectiveatreducingthetransactionpropagationdelaycomparedtotheexistingclusteringprotocol(LBC)thatweproposedinourpreviouswork.

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CatchMeIfYouCan:DetectingCompromisedUsersThroughPartialObservationonNetworksDerekWang(DeakinUniversity),ShengWen(DeakinUniversity),JunZhang(DeakinUniversity),SuryaNepal(Data61),YangXiang(DeakinUniversity),WanleiZhou(DeakinUniversity)

Peoplearesufferingfromarangeofrisksintheubiquitousnetworksofcurrentworld,suchasrumoursspreadinginsocialnetworks,computervirusespropagatingthroughouttheInternetandunexpectedfailureshappenedinSmartgrids.Weusuallymonitoronlyafewusersofdetectingvariousrisksduetotheresourceconstraintsandprivacyprotection.Thisleadstoacriticalproblemtodetectcompromiseduserswhoareoutofsurveillance.Inthispaper,weproposeariskassessmentmethodtoaddressthisproblem.Theaimistoassessthesecuritystatusofunmonitoredusersaccordingtothelimitedinformationcollectedfrommonitoredusersinnetworks.Therearetwoinnovativetechniquesdeveloped:First,weidentifythesourceofriskpropagationbyinverselydisseminatingrisksfromtheinfluenced(byrumours)orinfected(byviruses)monitoredusers.Weshowanewfindingthattheoneswhosynchronouslyreceivetheriskcopiesfromallmonitoredusersaremostlikelytobethesources.Second,weproposeamicroscopicmathematicalmodeltopresenttheriskpropagationfromtheexposedsources.Thismodelformsadiscriminanttoclassifythecompromisedusersfromothers.Forevaluations,wecollectthreerealnetworksonwhichwelaunchsimulatedriskpropagationandthensamplethestatusofmonitoredusers.Theexperimentresultsshowthatourmethodiseffectiveandtheresultofriskassessmentwellmatchestherealstatusoftheunmonitoredusers.

LocationPrivacyBreach:AppsAreWatchingYouinBackground DachuanLiu(CollegeofWilliam&Mary),XingGao(CollegeofWilliam&Mary),HainingWang(UniversityofDelaware)

SmartphoneuserscanconvenientlyinstallasetofappsthatprovideLocationBasedService(LBS)frommarkets.TheseLBS-basedappsfacilitateusersinmanyapplicationscenarios,buttheyraiseconcernsonthebreachofprivacyrelatedtolocationaccess.Smartphoneuserscanhardlyperceivelocationaccess,especiallywhenithappensinbackground.Incomparisontolocationaccessinforeground,locationaccessinbackgroundcouldresultinmoreseriousprivacybreachbecauseitcancontinuouslyknowauser’slocations.Inthispaper,westudytheproblemoflocationaccessinbackground,andespeciallyperformthefirstmeasurementofthisbackgroundactionontheGoogleappmarket.Ourinvestigationdemonstratesthatmanypopularappsconductlocationaccessinbackgroundwithinshortintervals.Thisenablestheseappstocollectauser’slocationtrace,fromwhichtheimportantpersonalinformation,PointsofInterest(PoIs),canberecognized.Wefurtherextractauser’smovementpatternfromthePoIs,andutilizeittomeasuretheextentofprivacybreach.ThemeasurementresultsalsoshowthatusingthecombinationofmovementpatternrelatedmetricsandtheotherPoIrelatedmetricscanhelpdetecttheprivacybreachinanearliermannerthanusingeitheroneofthemalone.

AndroidMalwareDetectionusingComplex-Flows FengShen(SUNYBuffalo),JustinDelVecchio(SUNYBuffalo),AzizMohaisen(SUNYBuffalo),StevenY.Ko(SUNYBuffalo),LukaszZiarek(SUNYBuffalo)

Thispaperproposesanewtechniquetodetectmobilemalwarebasedoninformationflowanalysis.Ourapproachexaminesthestructureofinformationflowstoidentifypatternsofbehaviorpresentinthemandwhichflowsarerelated,thosethatsharepartialcomputationpaths.WecallsuchflowsComplex-Flows,astheirstructure,patterns,andrelationsaccuratelycapturethecomplexbehaviorexhibitedbybothrecentmalwareandbenignapplications.N-gramanalysisisusedtoidentifyuniqueandcommonbehavioralpatternspresentinComplex-Flows.TheN-gramanalysisisperformedonsequencesofAPIcallsthatoccuralongComplex-Flows'controlflowpaths.Weshowtheprecisionofourtechniquebyapplyingittodifferentdatasetstotaling7,798apps.Thesedatasetsconsistofbothrecentandoldergenerationbenignandmaliciousappstodemonstratetheeffectivenessofourapproachacrossdifferentgenerationsofapps.

PrivacyImplicationsofDNSSECLook-asideValidation AzizMohaisen(SUNYBuffalo),ZhongshuGu(IBMResearch),KuiRen(SUNYBuffalo)

TocomplementDNSSECoperations,DNSSECLook-asideValidation(DLV)isdesignedforalternativeoff-pathvalidation.WhileDNSprivacyattractsalotofattention,theprivacyimplicationsofDLVarenotfullyinvestigatedandunderstood.Inthispaper,wetakeafirstin-depthlookintoDLV,highlightingitslaxspecificationsandprivacyimplications.Byperformingextensiveexperimentsoverdatasetsofdomainnamesundercomprehensiveexperimentalsettings,ourfindingsfirmlyconfirmtheprivacyleakagescausedbyDLV.WediscoverthatalargenumberofdomainsthatshouldnotbesenttoDLVserversarebeingleaked.Weexploretherootcauses,includingthelaxspecificationsofDLV.Wealsoproposetwoapproachestofixtheprivacyleakages.OurapproachesrequiretrivialmodificationstotheexistingDNSstandardsandwedemonstratetheircostintermsoflatencyandcommunication.

FlipNet:ModelingCovertandPersistentAttacksonNetworkedResources SudipSaha(VirginiaPolytechnicInstituteandStateUniversity),AnilVullikanti(VirginiaPolytechnicInstituteandStateUniversity),MahanteshHalappanavar(PacificNorthwestNationalLab)

Persistentandzero-dayattackshaveincreasedconsiderablyintherecentpastintermsofscaleandimpact.Securityexpertscannolongerrelyonlyonknowndefensesandtherebyprotecttheirresourcespermanently.Itisincreasinglycommonnowtoobserveattackersbeingabletorepeatedlybreaksystemsexploitingnewvulnerabilitiesanddefendershardeningsystemswithnewmeasures.Tomodelthisphenomenonoftherepeatedtakeoverofthecomputingresourcesbysystemadministratorsandmaliciousattackers,anovelgameframework,FlipIt,hasbeenproposedby(VanDijketal.2013)forasystemconsistingofasingleresource.Inthispaper,weextendthisanddevelopFlipNet,whichisarepeatedgameframeworkforanetworkedsystemofmultipleresources.Thisgameinvolvestwoplayers---adefenderandanattacker.Eachplayer'sobjectiveistomaximizeitsgain(i.e.,itscontroloverthenodesinthenetworkwithstealthymoves),whileminimizingthecostformakingthosemoves.Thisleadstoanovelandnaturalgameformulation,withaverycomplexstrategyspace,thatdependsonthenetworkstructure.WeshowthatfindingthebestresponsestrategyforboththedefenderandattackerisNP-hard.Inakeyresultinthisstudy,weshowthattheattacker'sgainforaninstanceofthegamehasatypeofdiminishingmarginalreturnproperty,whichleadstoanear-optimalalgorithmformaximizingtheattacker'sgain.Weexaminetheimpactofnetworkstructureonthestrategyspaceusingsimulations.

UnderstandingtheMarket-levelandNetwork-levelBehaviorsoftheAndroidMalwareEcosystem ChaoYang(Niara,Inc.),JialongZhang(IBMResearch),GuofeiGu(TexasA&MUniversity)

TheprevalenceofmalwareinAndroidmarketplacesisagrowingandsignificantproblem.MostexistingstudiesfocusondetectingAndroidmalwareordesigningnewsecurityextensionstodefendagainstspecifictypesofattacks.Inthispaper,weperformanempiricalstudyonanalyzingthemarket-levelandnetwork-levelbehaviorsoftheAndroidmalwareecosystem.Wefocusonstudyingwhetherthereareinterestingcharacteristicsofthosemarketaccountsthatdistributemalware

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andspecificnetworksthataremainlyutilizedbyAndroidmalwareauthors.WefurtherinvestigatecommunitypatternsamongAndroidmalwarefromtheperspectiveoftheirmarketaccountinfrastructureandremoteserverinfrastructure.Spurredbytheseanalysis,wedesignanovelcommunityinferencealgorithmtofindmoremaliciousappsbyexploitingtheircommunityrelationships.Byusingasmallseedset(50)ofknownmaliciousapps,wecaneffectivelyfindanotherextra20timesofmaliciousapps,whilemaintainingconsiderableaccuracyhigherthan94%.

EnGarde:Mutually-TrustedInspectionofSGXEnclaves. HaiNguyen(RutgersUniversity),VinodGanapathy(RutgersUniversity)

Intel’sSGXarchitectureallowscloudclientstocreateenclaves,whosecontentsarecryptographicallyprotectedbythehardwareevenfromthecloudprovider.Whilethisfeatureprotectstheconfidentialityandintegrityoftheclient’senclavecontent,italsomeansthatenclavecontentiscompletelyopaquetothecloudprovider.Thus,thecloudproviderisunabletoenforcepolicycomplianceonenclaves.Inthispaper,weintroduceEnGarde,asystemthatallowscloudproviderstoensureSLAcomplianceonenclavecontent.InEnGarde,cloudprovidersandclientsmutuallyagreeuponasetofpoliciesthattheclient’senclavecontentmustsatisfy.EnGardeexecuteswhentheclientprovisionstheenclave,ensuringthatonlypolicy-compliantcontentisloadedintotheenclave.EnGardeisabletoachieveitsgoalswithoutcompromisingthesecurityguaranteesofferedbytheSGX,andimposesnoruntimeoverheadontheexecutionofenclavecode.WehavedemonstratedtheutilityofEnGardebyusingittoenforceavarietyofsecuritypoliciesonenclavecontent.

TruthfulOnlineAuctionforCloudInstanceSubletting YifeiZhu(SimonFraserUniversity),SilveryFu(SimonFraserUniversity),JiangchuanLiu(SimonFraserUniversity),YongCui(TsinghuaUniversity)

DespitethatIaaSusersarebusyscalingup/outtheircloudinstancestomeettheever-increasingdemands,thedynamicsoftheirdemands,aswellasthecoarse-grainedbillingoptionsofferedbyleadingcloudproviders,haveledtosubstantialinstanceunderutilizationinbothtemporalandspatialdomains.Thispapertheoreticallyexaminesaninstancesublettingservice,whereunderutilizedinstancesareleasedtootherswithinuser-specifiedperiods.ServingasasecondarymarketthatcomplementstheexistinginstancemarketofIaaSproviders,wespecificallyidentifythetheoreticalchallengesininstancesublettingservices,anddesignanonlineauctionmechanismtomakeallocationandpricingdecisionsfortheinstancestobesublet.Ourmechanismguaranteestruthfulnessandindividualrationalitywiththebestpossiblecompetitiveratio.Extensivetrace-drivensimulationsshowthatourproposedmechanismachievessignificantperformancegainsinbothcostandsocialwelfare.

OnthePowerofWeakerPairwiseInteraction:Fault-TolerantSimulationofPopulationProtocols GiuseppeAntonioDiLuna(LaSapienza),PaolaFlocchini(UniversityofOttawa),TaisukeIzumi(NagoyaInstituteofTechnology),TomokoIzumi(CollegeofInformationScienceandEngineering),NicolaSantoro(CarletonUniversity),GiovanniViglietta(UniversityofOttawa)

Inthispaperweinvestigatethecomputationalpowerofpopulationprotocolsundersomeunreliableorweakerinteractionmodels.Moreprecisely,wefocusontwofeaturesrelatedtothepowerofinteractions:omissionfailuresandone-waycommunications.Westartourinvestigationbyprovidingacompleteclassificationofallthepossiblemodelsarisingfromtheaforementionedweaknesses,andestablishingthecomputationalhierarchyofthesemodels.Wethenaddressforeachmodelthefundamentalquestionofwhatadditionalpowerisnecessaryandsufficienttocompletelyovercomethemodel’sweaknessandmakeitabletosimulatefaultlesstwo-wayprotocols.Weanswerthisquestionbypresentingsimulatorsthatworkundercertainassumptionsandbyprovingthatsimulationisimpossiblewithoutsuchassumptions.

DistributedFaultTolerantLinearSystemSolversbasedonErasureCoding XuejiaoKang(PurdueUniversity--WestLafayette),DavidF.Gleich(PurdueUniversity--WestLafayette),AhmedSameh(PurdueUniversity--WestLafayette),AnanthGrama(PurdueUniversity--WestLafayette)

Wepresentefficientcodingschemesanddistributedimplementationsoferasurecodedlinearsystemsolvers.Erasurecodedcomputationsbelongtotheclassofalgorithmicfaulttoleranceschemes.Theyarebasedonaugmentinganinputdataset,executingthealgorithmontheaugmenteddataset,andintheeventofafault,recoveringthesolutionfromthecorrespondingaugmentedsolution.Thisprocesscanbeviewedasthecomputationalanalogoferasurecodedstorageschemes.Theproposedtechniquehasanumberofimportantbenefits:(i)asthehardwareplatformscalesinsizeandnumberoffaults,ourschemeyieldsincreasingimprovementinresourceutilization,comparedtotraditionalschemes;(ii)theproposedschemeiseasytocode–thecorealgorithmsremainthesame;and(iii)thegeneralschemeisflexible–accommodatingarangeofcomputationandcommunicationtradeoffs.Wepresentnewcodingschemesforaugmentingtheinputmatrixthatsatisfytherecoveryequationsoferasurecodingwithhighprobabilityintheeventofrandomfailures.Thesecodingschemesalsominimizefill(non-zeroelementsintroducedbythecodingblock),whilebeingamenabletoefficientpartitioningacrossprocessingnodes.Wedemonstrateexperimentallythatourschemeaddsminimaloverheadforfaulttolerance,yieldsexcellentparallelefficiencyandscalability,andisrobusttodifferentfaultarrivalmodels.

PreservingIncumbentUsers’PrivacyinExclusion-Zone-BasedSpectrumAccessSystems YanzhiDou(VirginiaTech),HeLi(VirginiaTech),KexiongZeng(VirginiaTech),JinshanLiu(VirginiaTech),YalingYang(VirginiaTech),BoGao(ChineseAcademyofSciences),KuiRen(SUNYBuffalo)

Dynamicspectrumaccess(DSA)techniquehasemergedasafundamentalapproachtomitigatethespectrumscarcityproblem.AsakeyformofDSA,thegovernmentisproposingtoreleasemorefederalspectrumforsharingwithcommercialwirelessusers.However,theflourishoffederalcommercialsharinghingesuponhowthefederalprivacyismanaged.IncurrentDSAproposals,thesensitiveexclusionzone(E-Zone)informationoffederalincumbentusers(IUs)needstobesharedwithaspectrumaccesssystem(SAS)torealizespectrumallocation.However,SASisnotnecessarilytrust-worthyforholdingthesensitiveIUE-Zonedata,especiallyconsideringthatFCCallowssomeindustrythirdparties(e.g.,Google)tooperateSASforbetterefficiencyandscalability.Therefore,thecurrentproposalsdissatisfytheIUs’privacyrequirement.Toaddresstheprivacyissue,thispaperpresentsanIU-privacypreservingSAS(IP-SAS)design,whichrealizesthespectrumallocationprocessthroughsecurecomputationoverciphertextbasedonhomomorphicencryptionsothatnoneoftheIUEZoneinformationisexposedtoSAS.ThispaperalsoproposesmechanismstopreventmaliciouspartiesfromcompromisingIP-SAS.Weprovetheprivacy-preservingpropertiesofIP-SASanddemonstratethescalabilityandpracticalityofIP-SASusingexperimentsbasedonreal-worlddata.EvaluationresultsshowthatIP-SAScanrespondanSU’sspectrumrequestin1.25secondswithcommunicationoverheadof17.8KB.

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Demonstration Track Paper Abstracts

Demo1:DistributedApplicationsCluster

LITMUS:TowardsMultilingualReportingofLandslides AibekMusaev(UniversityofAlabama),QixuanHou(GeorgiaInstituteofTechnology),YangYang(GeorgiaInstituteofTechnology),CaltonPu(GeorgiaInstituteofTechnology)

LITMUSisareal-timeonlineandopenlyaccessibleservicethatcollectshighqualityinformationonlandslideeventsfromsocialmedia.Thisserviceusesdisasterrelatedkeywords,suchas"landslide"and"mudslide",toanalyzemessagespostedbyEnglishspeakingusers.However,comprehensivecoverageofdisastersmustincludemultilingualsupportasthereareeventsthatarereportedinlanguagesotherthanEnglish.Wediscussandevaluatepossibleimplementationsofsuchsupportusing"native"and"translated"approaches."Native"approachinvolvesacompletereimplementationoftheexistinginfrastructureinanotherlanguagewhereasinthe"translated"approachtheexistinginfrastructurecanbeusedwithoutmodification.Asanillustration,wepresentademothatextendsLITMUStoimplementa"native"approachformultilingualreportingoflandslideevents.

Pythia:ASystemforOnlineTopicDiscoveryofSocialMediaPosts IoulianaLitou(AthensUniversityofEconomicsandBusiness),VanaKalogeraki(AthensUniversityofEconomicsandBusiness)

Socialmediaconstitutenowadaysoneofthemostcommoncommunicationmediums.Millionsofusersexploitthemdailytoshareinformationwiththeircommunityinthenetworkviamessages,referredas\emph{posts}.Themassivevolumeofinformationsharedisextremelydiverseandcoversavastspectrumoftopicsandinterests.Automaticallyidentifyingthetopicsofthepostsisofparticularinterestasthiscanassistinavarietyofapplications,suchaseventdetection,trendsdiscovery,expertfindingetc.However,designinganautomatedsystemthatrequiresnohumanagentparticipationtoidentifythetopicscoveredinpostspublishedinOnlineSocialNetworks(OSNs)presentsmanifoldchallenges.First,postsareunstructuredandcommonlyshort,limitedtojustafewcharacters.Thispreventsexistingclassificationschemestobedirectlyappliedinsuchcases,duetosparsenessofthetext.Second,newinformationemergesconstantly,hencebuildingalearningcorpusfrompastpostsmayfailtocapturetheeverevolvinginformationemerginginOSNs.ToovercometheaforementionedlimitationswehavedesignedPythia,anautomatedsystemforshorttextclassificationthatexploitstheWikipediastructureandarticlestoidentifythetopicsoftheposts.Thetopicdiscoveryisperformedintwophases.Inthefirststep,thesystemexploitsWikipediacategoriesandarticlesofthecorrespondingcategoriestobuildthetrainingcorpusforthesuppervisedlearning.Inthesecondstep,thetextofagivenpostisaugmentedusingatextenrichmentmechanismthatextendsthepostwithrelevantWikipediaarticles.Aftertheinitialstepsareperformed,wedeployk-NNclassifiertodeterminethetopic(s)coveredintheoriginalpost.

Data-drivenSerendipityNavigationinUrbanPlaces XiaoyuGe(UniversityofPittsburgh),AmeyaDaphalapurkar(UniversityofPittsburgh),ManaliShimpi(UniversityofPittsburgh),KohliDarpun(UniversityofPittsburgh),KonstantinosPelechrinis(UniversityofPittsburgh),PanosChrysanthis(UniversityofPittsburgh),DemetriosZeinalipour-Yazti(MaxPlanckInstituteforInformaticsandUniversityofCyprus)

WiththeproliferationofmobilecomputingandtheabilitytocollectdetaileddatafortheurbanenvironmentanumberofsystemsthataimatprovidingPointsofInterest(POIs)andtourrecommendationshaveappeared.Theoverwhelmingmajorityofthesesystemsaimsatprovidinganoptimalrecommendation,whereoptimalityreferstoobjectivesofminimizingthedistancetobecoveredormaximizingthequalityofthePOIsrecommended.Amajorproblemisthatbyfocusingontheoptimizationoftheseobjectives,thereremainslittleroomtotheuserforserendipity.Urbanandsocialscientistshaveidentifiedserendipity,i.e.,theabilitytocomeacrossunexpectedplaces,asafeaturethatmakesacitylivable.Inthiswork,weintroduceaprototypeofanexperimentalplatformforevaluatingvenuerecommendationalgorithmsbyprovidinginformativetourrecommendationsbasedonthesuggestedvenues.OurprototypesystemintegratesthenotionofserendipityinurbannavigationatboththevenueaswellastherouterecommendationlevelwithoutcompromisingthequalityanddiversityoftherecommendedPOIs.Inaddition,oursystemallowstheusertouploadtheirownalgorithmsandexploretheirperformanceascomparedtomanywell-knownalgorithms.

TowardAnIntegratedApproachtoLocalizingFailuresinCommunityWaterNetworks(DEMO) QingHan(UniversityofCaliforniaIrvine),PhuNguyen(UniversityofCaliforniaIrvine),RonaldT.Eguchi(ImageCat,Inc.),Kuo-LinHsu(UniversityofCaliforniaIrvine),NaliniVenkatasubramanian(UniversityofCaliforniaIrvine)

Wepresentacyber-physical-human(CPHS)distributedcomputingframework,AquaSCALE,forgathering,analyzingandlocalizinganomalousoperationsofincreasinglyfailure-pronecommunitywaterservices.Today,detectionofwaterpipeleakstakeshourstodays.AquaSCALEleveragesdynamicdatafrommultipleinformationsourcesincludingIoT(InternetofThings)sensingdata,geophysicaldata,humaninputandsimulation/modelingenginestocreateasensor-simulation-dataintegrationplatformthatcanlocatemultiplesimultaneouspipefailuresatfinelevelofgranularitywithhighlevelofaccuracyanddetectiontimereducedbyordersofmagnitude(fromhours/daystominutes).

Demo2:SecurityandPrivacyCluster

PrivateGraph:ACloud-CentricSystemforPrivacy-PreservingSpectralAnalysisofLargeEncryptedGraphs SagarSharma(WrightStateUniversity),KekeChen(WrightStateUniversity)

Graphdatasetshaveinvaluableuseinbusinessapplicationsandscientificresearch.Becauseofthegrowingsizeanddynamicallychangingnatureofgraphs,graphdataownersmaywanttousepubliccloudinfrastructurestostore,process,andperformgraphanalytics.However,whenoutsourcingdataandcomputation,dataownersareatburdentodevelopmethodstopreservedataprivacyanddataownershipfromcuriouscloudproviders.Thisdemonstrationexhibitsaprototypesystemforprivacy-preservingspectralanalysisframeworkforlargegraphsinpublicclouds(PrivateGraph)thatallowsdataownerstocollectgraphdatafromdatacontributors,andstoreandconductsecuregraphspectralanalysisinthecloudwithpreservedprivacyandownership.Thisdemosystemletsitsaudienceinteractivelylearnthemajorcloud-clientinteractionprotocols:theprivacy-preservingdatasubmission,thesecureLanczosandNystromapproximateeigen-decompositionalgorithmsthatworkoverencrypteddata,andtheoutcomeofanimportantapplicationofspectralanalysis-spectralclustering.Intheprocessofdemonstrationtheaudiencewillunderstandtheintrinsicrelationshipamongstcosts,resultquality,privacy,andscalabilityoftheframework.

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IoTSentinelDemo:AutomatedDevice-TypeIdentificationforSecurityEnforcementinIoT MarkusMiettinen(TechnischeUniversitatDarmstadt),SamuelMarchal(AaltoUniversity),IbbadHafeez(universityofhelsinki),TommasoFrassetto(TechnischeUniversitatDarmstadt),N.Asokan(AaltoUniversity),Ahmad-RezaSadeghi(TechnischeUniversitatDarmstadt),SasuTarkoma(UniversityofHelsinki)

TheemergenceofnumerousnewmanufacturersproducingdevicesfortheInternet-of-Things(IoT)hasgivenrisetonewsecurityconcerns.ManyIoTdevicesexhibitsecurityflawsmakingthemvulnerableforattacksandmanufacturershavedifficultiesinprovidingappropriatesecuritypatchestotheirproductsinatimelyanduser-friendlymanner.Inthispaper,wepresentourimplementationofIOTSENTINEL,whichisasystemaimedatprotectingtheuser’snetworkfromvulnerableIoTdevices.IOTSENTINELautomaticallyidentifiesvulnerabledeviceswhentheyarefirstintroducedtothenetworkandenforcesappropriatetrafficfilteringrulestoprotectotherdevicesfromthethreatsoriginatingfromthevulnerabledevices.

RogueAccessPointDetectorUsingCharacteristicsofChannelOverlappingin802.11nRhonghoJang(INHAuniversityofKorea),JeonilKang(INHAuniversity),AzizMohaisen(SUNYBuffalo),DaehunNyang(DepartmentofComputerandInformationEngineering,INHAUniversity,Incheon,Korea)

Inthiswork,weintroduceapowerfulhardwarebasedrogueaccesspoint(PrAP),whichcanrelaytrafficbetweenalegitimateAPandawirelessstationbackandforth,andactasaman-in-the-middleattacker.OurPrAPisbuiltoftwodedicatedwirelessroutersinterconnectedphysically,andcanrelaytrafficrapidlybetweenastationandalegitimateAP.Throughextensiveexperiments,wedemonstratethatthestateof-the-arttime-basedrogueAP(rAP)detectorscannotdetectourPrAP,althougheffectiveagainstsoftware-basedrAP.TodefendagainstPrAPs,weproposePrAP-Hunterbasedonintentionalchannelinterference.PrAP-Hunterishighlyaccurate,evenunderheavytrafficscenarios.Usingahigh-performance(desktop)andlow-performance(mobile)experimentalsetupsofourPrAPHunterinvariousdeploymentscenarios,wedemonstratecloseto100%ofdetectionrate,comparedto60%detectionratebythestate-of-the-art.WeshowthatPrAP-Hunterisfast(takes5-10sec),doesnotrequireanypriorknowledge,andcanbedeployedinthewildbyrealworldexperimentsat10coffeeshops.Keywords.Intrusiondetection,WirelessLAN,RogueAP,channelinterference,IEEE802.11n.

ReverseCloak:AReversibleMulti-levelLocationPrivacyProtectionSystem ChaoLi(UniversityofPittsburgh),BalajiPalanisamy(UniversityofPittsburgh),AravindKalaivanan(UniversityofPittsburgh),SriramRaghunathan(UniversityofPittsburgh)

Withthefastpopularizationofmobiledevicesandwirelessnetworks,alongwithadvancesinsensingandpositioningtechnology,wearewitnessingahugeproliferationofLocation-basedServices(LBSs).LocationanonymizationreferstotheprocessofperturbingtheexactlocationofLBSusersasacloakingregionsuchthatauser'slocationbecomesindistinguishablefromthelocationofasetofotherusers.However,existinglocationanonymizationtechniquesfocusprimarilyonsinglelevelunidirectionalanonymization,whichfailstocontroltheaccesstothecloakingdatatoletdatarequesterswithdifferentprivilegesgetinformationwithvaryingdegreesofanonymity.Inthisdemonstration,wepresentatoolkitforReverseCloak,alocationperturbationsystemtoprotectlocationprivacyoverroadnetworksinamulti-levelreversiblemanner,consistingofan'Anonymizer'GUItoadjusttheanonymizationsettingsandvisualizethemultilevelcloakingregionsoverroadnetworkforlocationdataownersanda'De-anonymizer'GUItode-anonymizethecloakingregionanddisplaythereducedregionoverroadnetworkforlocationdatarequesters.Withthetoolkit,wedemonstratethepracticalityandeffectivenessoftheReverseCloakapproach.

Demo3:CloudsandVirtualizationCluster

Hopsworks:ImprovingUserExperienceandDevelopmentonHadoopwithScalable,StronglyConsistentMetadata MahmoudIsmail(KTH-RoyalInstituteofTechnology),ErmiasGebremeskel(RISESICS),TheofilosKakantousis(RISESICS),GautierBerthou(RISESICS),JimDowling(KTH-RoyalInstituteofTechnology)

Hadoopisapopularsystemforstoring,managing,andprocessinglargevolumesofdata,butithasbare-bonesinternalsupportformetadata,asmetadataisabottleneckandlessmeansmorescalability.Theresultisascalableplaformwithrudimentaryaccesscontrolthatisneitheruser-nordeveloper-friendly.Also,metadataservicesthatarebuiltonHadoop,suchasSQL-on-Hadoop,accesscontrol,dataprovenance,anddatagovernancearenecessarilyimplementedaseventuallyconsistentservices,resultinginincreaseddevelopmenteffortandmorebrittlesoftware.Inthispaper,wepresentanewproject-basedmulti-tenancymodelforHadoop,builtonanewdistributionofHadoopthatprovidesadistributeddatabasebackendfortheHadoopDistributedFilesystem’s(HDFS)metadatalayer.WeextendHadoop’smetadatamodeltointroduceprojects,datasets,andproject-usersasnewcoreconceptsthatenableauser-friendly,UI-drivenHadoopexperience.Asourmetadataserviceisbackedbyatransactionaldatabase,developerscaneasilyextendmetadatabyaddingnewtablesandensurethestrongconsistencyofextendedmetadatausingbothtransactionsandforeignkeys.

IsolationinDockerthroughLayerEncryption IoannisGiannakopoulos(NationalTechnicalUniversityofAthens),KonstantinosPapazafeiropoulos(NationalTechnicalUniversityofAthens),KaterinaDoka(NationalTechnicalUniversityofAthens),NectariosKoziris(NationalTechnicalUniversityofAthens)

Containersareconstantlygaininggroundinthevirtualizationlandscapeasalightweightandefficientalternativetohypervisor-basedVirtualMachines,withDockerbeingthemostsuccessfulrepresentative.Dockerreliesonunion-capablefilesystems,whereanyactionperformedtoabaseimageiscapturedasanewfilesystemlayer.ThisstrategyallowsdeveloperstoeasilypackapplicationsintoDockerimagelayersanddistributethemviapublicregistries.However,thisimagecreationanddistributionstrategydoesnotprotectsensitivedatafrommaliciousprivilegedusers(e.g.,registryadministrator,cloudprovider),sinceencryptionisnotnativelysupported.WeproposeanddemonstrateamechanismforsecureDockerimagemanipulationthroughoutitslifecycle:Thecreation,storageandusageofaDockerimageisbackedbyadata-at-restmechanism,whichmaintainssensitivedataencryptedondiskandencrypts/decryptsthemon-the-flyinordertopreservetheirconfidentialityatalltimes,whilethedistributionandmigrationofimagesisenhancedwithamechanismthatencryptsonlyspecificlayersofthefilesystemthatneedtoremainconfidentialandensuresthatonlylegitimatekeyholderscandecryptthemandreconstructtheoriginalimage.Througharichinteractionwithoursystemtheaudiencewillexperiencefirst-handhowsensitiveimagedatacanbesafelydistributedandremainencryptedatthestoragedevicethroughoutthecontainer'slifetime,bearingonlyamarginalperformanceoverhead.

Dela-SharingLargeDatasetsbetweenHadoopClusters AlexandruA.Ormenisan(KTHRoyalInstituteofTechnology),JimDowling(KTHRoyalInstituteofTechnology)

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Bigdatahas,inrecentyears,revolutionisedanever-growingnumberoffields,frommachinelearningtoclimatesciencetogenomics.Thecurrentstate-of-the-artforstoringlargedatasetsiseitherobjectstoresordistributedfilesystems,withHadoopbeingthedominantopen-sourceplatformformanaging‘BigData’.Existinglarge-scalestorageplatforms,however,lacksupportfortheefficientsharingoflargedatasetsovertheInternet.Thosesystemsthatarewidelyusedforthedisseminationoflargefiles,likeBitTorrent,needtobeadaptedtohandlechallengessuchasnetworklinkswithbothhighlatencyandhighbandwidth,andscalablestoragebackendsthatareoptimisedforstreamingandnotrandomaccess.Inthispaper,weintroduceDela,apeer-to-peerdata-sharingserviceintegratedintotheHopsHadoopplatformthatprovidesanend-to-endsolutionfordatasetsharing.Delaisdesignedforlarge-scalestoragebackendsanddatatransfersthatarebothnon-intrusivetoexistingTCPnetworktrafficandprovidehighernetworkthroughputthanTCPonhighlatency,highbandwidthnetworklinks,suchastransatlanticnetworklinks.Delaprovidesapluggablestoragelayer,implementingtwoalternativewaysforclientstoaccessshareddata:streamprocessingofdataasitarriveswithKafka,andtraditionalofflineaccesstodatausingtheHadoopDistributedFilesystem.DelaisthefirststepfortheHadoopplatformtowardscreatinganopendatasetecosystemthatsupportsuser-friendlypublishing,searching,anddownloadingoflargedatasets.

InVivoEvaluationoftheSecureOpportunisticSchemesMiddlewareusingaDelayTolerantSocialNetworkCoreyE.Baker(UniversityofCaliforniaSanDiego),AllenStarke(UniversityofFlorida),TanishaG.Hill-Jarrett(UniversityofFlorida),JaniseMcNair(UniversityofFlorida)

Overthepastdecade,onlinesocialnetworks(OSNs)suchasTwitterandFacebookhavethrivedandexperiencedrapidgrowthtoover1billionusers.AmajorevolutionwouldbetoleveragethecharacteristicsofOSNstoevaluatetheeffectivenessofthemanyroutingschemesdevelopedbytheresearchcommunityinreal-worldscenarios.Inthisdemonstration,weshowcasetheSecureOpportunisticSchemes(SOS)middlewarewhichallowsdifferentroutingschemestobeeasilyimplementedrelievingtheburdenofsecurityandconnectionestablishment.ThefeasibilityofcreatingadelaytolerantsocialnetworkisdemonstratedbyusingSOStoenableAlleyOopSocial,asecuredelaytolerantnetworkingresearchplatformthatservesasareal-lifemobilesocialnetworkingapplicationforiOSdevices.AlleyOopSocialallowsuserstointeract,publishmessages,anddiscoverothersthatsharecommoninterestsinanintermittentnetworkusingBluetooth,peer-to-peerWiFi,andinfrastructureWiFi.

Demo4:DistributedSystemsandNetworkingCluster

ScalingandLoadTestingLocation-basedPublishandSubscribe BertilChapuis(UniversityofLausanne),BenoîtGarbinato(UniversityofLausanne)

TheriseoftheInternetofthings(IoT)posesmassivescalabilityissuesforlocation-basedservices.Moreparticularly,location-awarepublishandsubscribeservicesarestrugglingtoscaleoutthecomputationofmatchesbetweenpublicationsandsubscriptionsthatcontinuouslyupdatetheirlocation.Inthisdemonstrationpaper,weproposeanoveldistributedandhorizontallyscalablearchitectureforlocation-awarepublishandsubscribe.Ourmiddlewarearchitecturereliesonamultisteproutingmechanismbasedonconsistenthashingandrangepartitioning.Todemonstrateitsscalability,wepresentatrafficdatagenerator,which,incontrasttoexistinggenerators,canbeusedtoperformreal-timeloadtests.Finally,weshowthatourarchitecturecanbedeployedonasmall10-nodeclusterandcanprocessupto80,000locationupdatespersecondproducing25,000matchesperseconds.

ADistributedEvent-centricCollaborativeWorkflowsDevelopmentSystemforIoTApplication YongyangCheng(BUPT),ShuaiZhao(BUPT),BoCheng(BUPT),ShouluHou(BUPT),XiuleiZhang(BUPT),JunliangChen(BUPT)

TherapiddevelopmentofInternetofThings(IoT)attractsgrowingattentionfrombothindustryandacademia.IoTseamlesslyconnectsthephysicalworldandcyberspaceviavarioussensors.Itismoreworthforustopayattentiontothemechanismoftheeventstoworkcollaborativelyratherthanthosestandalonesensors.Inthispaper,wepresentaDistributedEvent-centricCollaborativeWorkflowsdevelopmentsystemforIoTapplication,calledDECW.Itsupportslooselycoupledevent-basedinteractionbetweenprocesses,whichenablesreal-timeresponsetoeventsfromthephysicalworld.Unliketraditionalcentralizedcontrolflowmode,theinteractionbetweenprocessesinDECWisconstrainedbytheeventinterface.Userscoulddynamicallyadjusttheinterfacebetweenprocesseswithoutmodifyingtheinternallogicoftheprocess.Inaddition,DECWsystemprovidesafulllifecycleforthedevelopmentandoperationoftheIoTapplication,includinggraphicalcreationofprocesses,dynamicdefinitionoftheprocessinteractioninterfaces,logicalvalidation,distributedpackaginganddeployment,parallelexecution,andreal-timemonitoringandmanagingtherunningstatusoftheIoTapplication.

IncentiveMechanismforData-CentricMessageDeliveryinDelayTolerantNetworks HimanshuJethawa(MissouriUniversityofScienceandTechnology),SanjayMadria(MissouriUniversityofScienceandTechnology)

Akeyissueindelaytolerantnetworks(DTN)istofindtherightnodetostoreandrelaymessages.Weconsidermessagesannotatedwiththeuniquekeywordsdescribingthemessagesubject,andnodesalsoaddskeywordstodescribetheirmissioninterests,priorityandtheirtransientsocialrelationship(TSR).Tooffsetresourcecosts,anincentivemechanismisdevelopedovertransientsocialrelationshipswhichenrichenroutemessagecontentandmotivatebettersemanticallyrelatednodestocarryandforwardmessages.Theincentivemechanismensuresavoidanceofcongestionduetouncooperativeorselfishbehaviorofnodes.

PerformanceOfCognitiveWirelessChargerForNear-FieldWirelessChargingSang-YoonChang(UniversityofColorado,ColoradoSpringsandAdvancedDigitalSciencesCenter),SristiLakshmiSravanaKumar(AdvancedDigitalSciencesCenter),Yih-ChunHu(UniversityofIllinoisatUrbana-Champaign)

WirelesschargingprovidesaconvenientwaytochargevariousmobileandIoTdevices.State-of-the-artwire-lesschargingsystemsoperateataparticularfrequencyandarecontrolledbyexplicitnetworkingwiththepower-receivingdevices.However,controlbyexplicitnetworkingisnotdesignedtocopewiththevariationsinthepower-receivingdevicesplacementsandalignments.Thisisespeciallymoresignificantinnear-fieldandpseudo-tightlycoupledchargingapplicationsasmorechargingpadsarebeingdeployedinthepublicdomainsandservingheterogeneousclients.Weestablishthatfrequencycontrolachievesbetterperformancegainsininductive-couplingchargingapplicationsandisalsosensitivetothevariationsintheplacementandalignmentbetweenthepower-transmittingandthepower-receivingcoils.Inthisdemo,weshowtheimpactinpowertransferperformancecausedbythevariationsintheplacementandalignmentbetweenthepower-transmittingandthepower-receivingcoilsandshowcasetheperformanceofourcognitivewirelesscharger(CWC),whichadaptivelycontrolstheoperatingfrequencyinreal-timeusingimplicitfeedbackforoptimaloperations.

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Poster Track Paper Abstracts

Poster1:DistributedApplicationsCluster

TowardVehicleSensing:Anintegratedapplicationwithsparsevideocamerasandintelligenttaxicabs YangWang(UniversityofScienceandTechnologyofChina),WujiChen(UniversityofScienceandTechnologyofChina),WeiZheng(Sanofi-AventisUSLLC),HeHuang(SoochowUniversity),WenZhang(UniversityofScienceandTechnologyofChina),HengchangLiu(UniversityofScienceandTechnologyofChina)

Duetothesparsedistributionofroadvideosurveillancecameras,precisetrajectorytrackingforvehiclesremainsachallengingtask.Tothebestofourknowledge,noneofthepreviousresearchconsideredusingon-roadtaxicabsasmobilevideosurveillancecamerasandroadtrafficflowpatterns,thereforenotsuitableforrecoveringtrajectoriesofvehicles.Withthisinsight,wemodelthetraveltime-costofaroadsegmentduringvarioustimeperiodspreciselywithLNDs(LogarithmicNormalDistributions),thenuseLSNDs(LogSkewNormalDistributions)toapproximatethetime-costofanurbantripduringvarioustimeperiods.Weproposeanapproachtocalculatepossiblelocationandtimedistributionofthevehicle,selectthetaxicabtoverifythedistributionbyuploadingandcheckingvideoclipsofthistaxicab,finallyrefinetherestoringtrajectoryinarecursivemanner.Weevaluateoursolutiononreal-worldtaxicabandroadsurveillancesystemdatasets.Experimentalresultsdemonstratethatourapproachoutperformsalternativesolutionsintermsofaccuracyratioofvehicletracking.

SegmentationofTimeSeriesbasedonKineticCharacteristicsforStorageConsumptionPrediction BeibeiMiao(Baidu,Inc),YuChen(Baidu,Inc),XueboJin(SchoolofComputerandInformationEngineering,BeijingTechnologyandBusinessUniversity),BoWang(Baidu,Inc),XianpingQu(Baidu,Inc),DongWang(Baidu,Inc),ShiminTao(Baidu,Inc),ZhiZang(Baidu,Inc)

TheInternetservicesgeneratehugeamountofdata,whichrequirelargespaceforstorage.Determiningdevicepurchaseplanturnsouttobeveryimportantfortheserviceproviders.Under-purchasingmightleadtodataloss,whileover-purchasingwouldresultinwaste.Inthispaper,weproposealinearregressionbasedapproachtopredictthestoragedemandaccordingtothetimeseriesofthestorageconsumption.Wepartitionedthestorageconsumptiontimeseriesintoseverallinearsegments,andperformpredictiononthelastsegmentusinglinearregression.Sincethepositionofturningpointsbetweenadjacentsegmentsandthetotalnumberofthesegmentsarebothunknown,howtoachievetheonlinesegmentationbecomesabigchallenge.Aimingtosolvethisproblem,wecarriedouttheKalmanAnovasegmentationmethod.Experimentresultsshowthatourmethodhasgoodaccuracyinprecision,recallandF-measurevalues.Moreover,themethodisabletosegmentnonlineartimeseriesaswell,suggestingapotentialwiderapplication.TheproposedmethodhasbeendeployedinBaiduInc.andsavesabout45thousanddollarsinoneofitsdevicepurchaseprogram.

AMulti-stageHierarchicalWindowModelwithApplicationtoReal-TimeGraphAnalysis SachiniJayasekara(UniversityofMelbourne),ShanikaKarunasekera(UniversityofMelbourne),AaronHarwood(UniversityofMelbourne)

Thedynamicnatureofreal-worldnetworks,suchassocialnetworksandcommunicationnetworks,hasincreasedthefocustowardsreal-timedynamicgraphanalysis.Observationsmadebasedonreal-timeanalysisofdynamicgraphsreflectthelatestpropertiesofthegraphandhavethemostvalueinreal-timeanalysis.Computinggraphpropertiesoflarge-scale,fast-evolvinggraphsinreal-timeischallengingdue,notonlytothehighcomputationalandmemorycost,butalsototheunderstandingoftheresultwithrespecttothedatafromwhichitwasderived.Thispaperproposesamulti-stagehierarchicalwindowmodelthatcanaidinrigorousunderstandingofcomplicatedreal-timeresultsandweapplyittogenerategraphsbasedonreal-timeupdatesalongwithperiodiccomputationsongraphsnapshotsforprocessingdynamicgraphs.Moreover,thepaperdiscussestheutilizationofparallelwindowcomputation.ThepaperevaluatesthehierarchicalmodelthroughanalyzinggraphsformedbycooccurringhashtagsinaTwitterdata-stream.

DynamicPricingatElectricVehicleChargingStationsforQueueingDelayReduction XiaoshanSun(UniversityofScienceandTechnologyofChina),PengXu(UniversityofScienceandTechnologyofChina),JinyangLi(UniversityofScienceandTechnologyofChina),HengchangLiu(UniversityofScienceandTechnologyofChina),WeiZheng(Sanofi-Aventis)

Theresearchofelectricvehicles(EVs)hasgainedmoreandmoreattentioninrecentyearsinbothindustryandacademia,andnewregistrationsofEVsincreaserapidly,whilethelongdelayattheconvenientbutcrowdedchargingstationsmaydiscouragemanydriversfromswitchingtoEVs.Toaddresstheproblems,weproposeanoveldynamicpricingpolicythatallowschargingstationstoadjusttheirservicefeesinrealtimebasedontheloadatthestations.Inourwork,theselectionofdriversismodeledbyanewdissatisfactionfunctionwithmultiplevariables,whichcanbeeasilyvalidatedandimprovedbyrealapplications,andoursolutionisevaluatedfromtherealworlde-chargedataset.Tothebestofourknowledge,thisisthefirstworkthatconsidersdynamicservicefeesamongvariouschargingstationsforloadbalancingandreductionofqueueingdelay.Thismakesourworkmorerealisticandbeneficial.

PairwiseRankingAggregationbyNon-interactiveCrowdsourcingwithBudgetConstraints ChangjiangCai(StevensInstituteofTechnology),HaipeiSun(StevensInstituteofTechnology),BoxiangDong(MontclairStateUniversity),BoZhang(StevensInstituteofTechnology),TingWang(LehighUniversity),WendyHuiWang(StevensInstituteofTechnology)

Crowdsourcedrankingalgorithmsaskthecrowdtocomparetheobjectsandinferthefullrankingbasedonthecrowdsourcedpairwisecomparisonresults.Inthispaper,weconsiderthesettinginwhichthetaskrequesterisequippedwithalimitedbudgetthatcanaffordonlyasmallnumberofpairwisecomparisons.Tomaketheproblemmorecomplicated,thecrowdmayreturnnoisycomparisonanswers.Weproposeanapproachtoobtainagood-qualityfullrankingfromasmallnumberofpairwisepreferencesintwosteps,namelytaskassignmentandresultinference.Inthetaskassignmentstep,wegeneratepairwisecomparisontasksthatproduceafullrankingwithhighprobability.Intheresultinferencestep,basedonthetransitivepropertyofpairwisecomparisonsandtruthdiscovery,wedesignanefficientheuristicalgorithmtofindthebestfullrankingfromthepotentiallyconflictivepairwisepreferences.Theexperimentresultsdemonstratetheeffectivenessandefficiencyofourapproach.

Buffer-BasedReinforcementLearningforAdaptiveStreaming YueZhang(SUNYBinghamton),YaoLiu(SUNYBinghamton)

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Adaptivestreamingimprovesuser-perceivedqualitybyalteringthestreamingbitratedependingonnetworkconditions,tradingreducedvideobitratesforreducedstalltimes.Existingadaptationapproaches,e.g.,rate-based,bufferbased,eitherrelyheavilyonaccuratebandwidthpredictionorcanbeoverly-conservativeaboutvideobitrates.Inthiswork,weproposeareinforcementlearningapproachtochoosethesegmentqualityduringplayback.Thisapproachusesonlythebufferstateinformationandoptimizesforameasureofuser-perceivedstreamingquality.SimulationresultsshowthatourproposedapproachachievesbetterQoEthanrate-,buffer-basedapproaches,aswellasotherreinforcementlearningapproaches.

Thecaseforusingcontent-centricnetworkingfordistributinghigh-energyphysicssoftware MohammadAlhowaidi(UniversityofNebraska-Lincoln),ByravRamamurthy(UniversityofNebraska-Lincoln),BrianBockelman(UniversityofNebraska-Lincoln),DavidSwanson(UniversityofNebraska-Lincoln)

NamedDataNetworking(NDN)isoneofthepromisingfutureinternetarchitectures,whichfocusesonthedataratherthanitslocation(IP/host-basedsystem).NDNhasseveralcharacteristicswhichfacilitateaddressingandroutingthedata:fail-over,in-networkcachingandloadbalancing.Thismakesitusefulinareassuchasmanagingscientificdata.TheCMSexperimentontheLargeHadronCollider(LHC)hasadataaccessproblemamenabletocontent-centricnetworking.CERNVirtualMachineFileSystem(CVMFS)isusedbyHighEnergyPhysics(HEP)communityforworldwidesoftwaredistribution.CVMFSmaintainitsdatabyusingcontentaddressablestorage,whichmakesitsuitableforNDN.Inthispaper,weinvestigatethepossibilitiesofusingacontentcentricnetworkingarchitecturesuchasNDNondistributingCMSsoftware.

LAVEA:Latency-awareVideoAnalyticsonEdgeComputingPlatformShanheYi(CollegeofWilliamandMary),ZijiangHao(CollegeofWilliamandMary),QingyangZhang(WayneStateUniversity),QuanZhang(WayneStateUniversity),WeisongShi(WayneStateUniversity),QunLi(CollegeofWilliamandMary)

WepresentLAVEA,asystembuiltforedgecomputing,whichoffloadscomputationtasksbetweenclientsandedgenodes,collaboratesnearbyedgenodes,toprovidelow-latencyvideoanalyticsatplacesclosertotheusers.Wehaveutilizedanedge-firstdesigntominimizetheresponsetime,andcomparedvarioustaskplacementschemestailedforinter-edgecollaboration.Ourresultsrevealthattheclient-edgeconfigurationhastaskspeedupagainstlocalorclient-cloudconfigurations.

CompleteToleranceRelationbasedFillingAlgorithmusingSpark JinglingYuan(WuhanUniversityofTechnology),YaoXiang(WuhanUniversityofTechnology),XianZhong(WuhanUniversityofTechnology),MinchengChen(WuhanUniversityofTechnology),TaoLi(UniversityofFlorida)

Withtheadventofcloudcomputing,renewableenergyisintegratedintodatacenterpowersupplysystemsincreasingly.Thepowerstatisticscollectionmaynotbeavailableduetotheinstabilityofrenewableenergy,whichresultsinincompletedata.Theincompleteenergydatawillsignificantlydisturbthemanagementofdatacenters.Wefurtherproposeafillingalgorithmbasedoncompletetoleranceclass.Thealgorithmexpandsthetraditionaltolerancerelation,andfillsthemissingvaluesoftheenergydata,whichensuresthedataintegrity.Bytakinggoodadvantageofin-MemoryComputing,WefurtherparallelizeandoptimizeouralgorithmusingSpark.Theexperimentresultsdemonstratethatouralgorithmoutperformsothergeneralfillingalgorithmsintermsoffillingaccuracy.Theproposedalgorithmalsoshowsgoodperformanceasthemissingraterisesup.

Poster2:SecurityandPrivacyCluster

TowardsSecurePublicDirectoryforPrivacy-PreservingDataSharingAminFallahi(SyracuseUniversity),XiLiu(SyracuseUniversity),YuzheTang(SyracuseUniversity),ShuangWang(UCSD),RuiZhang(ChineseAcademyofSciences)

Inemergingfederateddatabasesystems,suchasHealthInformationExchange(orHIE),animportantyetunderstudiedproblemistheprivacy-preservingsharingofpersonalrecordsamongautonomousdataowners.Thegoalposestechnicaldesignchallenges,includingtheassuredprivacypreservationunderbackground-knowledgeattacks,andscalableandsecuremulti-partycomputationsonprivatebig-datainalarge-scalesystem.Totacklethechallenges,weproposeaprotocol,multi-partydeterministicnoisingorMPDN,whichdeterministicallyinjectsnoisestothepublishedmeta-datawhilestayingawareofthebackgroundknowledge.Italsooptimizestheperformanceofmulti-partycomputation(orMPC)bypre-computationonthepublicbackgroundknowledge.Thepre-computationexhibitsdata-levelparallelismandweleveragegeneral-purposecomputingongraphicsprocessingunits(GPGPU)inourimplementationtoexploittheparallelismandtofurtheroptimizeperformance.Theproposedprotocolisimplementedonopen-sourceMPCsoftware(i.e.,GMW)anditsefficiencywithaspeedupofmorethananorderofmagnitudeisdemonstratedinageo-distributedsetting.Throughevaluationonreal-worlddatasets,theassuranceofprivacypreservationisalsoverified.

AnonymousRoutingtoMaximizeDeliveryRatesinDTNs KazuyaSakai(TokyoMetropolitanUniversity),Min-TeSun(NationalCentralUniversity),Wei-ShinnKu(AuburnUniversity),JieWu(TempleUniversity)

Securityandprivacyissuesareconsideredtobetwoofthemostsignificantconcernstoorganizationsandindividualsusingmobileapplications.Inthispaper,weseektoaddressanonymouscommunicationsindelaytolerantnetworks(DTNs).Whilemanydifferentanonymousroutingprotocolshavebeenproposedforadhocnetworks,tothebestofourknowledge,onlyvariantsofonion-basedroutinghavebeentailoredforDTNs.Sinceeachtypeofanonymousroutingprotocolhasitsadvantagesanddrawbacks,thereisnosingleanonymousroutingprotocolforDTNsthatcanadapttothedifferentlevelsofsecurityrequirements.Inthispaper,wefirstdesignasetofanonymousroutingprotocolsforDTNs,calledanonymousEpidemicandzone-basedanonymousrouting,basedontheoriginalanonymousroutingprotocolsforadhocnetworks.Then,weproposeaframeworkofanonymousrouting(FAR)forDTNs,whichsubsumesalltheaforementionedprotocols.Bytuningitsparameters,theproposedFARisabletooutperformonionbased,anonymousEpidemic,andzone-basedrouting.Inaddition,numericalanalysesforthetraceablerateandnodeanonymitymodelsarebuilt.Extensivesimulationsusingrandomlygeneratedgraphsaswellasrealtracesareconductedtodemonstratethatgivenappropriateparametersettings,ourFARoutperformsalltheexistinganonymousroutingprotocolsforDTNs.

EvaluatingConnectionResiliencefortheOverlayNetworkKademliaHennerHeck(UniversitätKassel),OlgaKieselmann(UniversitätKassel),ArnoWacker(UniversitätKassel)

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Kademliaisadecentralizedoverlaynetwork,uptonowmainlyusedforhighlyscalablefilesharingapplications.Duetoitsdistributednature,itisfreefromsinglepointsoffailure.Communicationcanhappenoverredundantnetworkpaths,whichmakesinformationdistributionwithKademliaresilientagainstfailingnodesandattacks.Inthispaper,wesimulateKademlianetworkswithvaryingparametersandanalyzethenumberofnode-disjointpaths.Withourresults,weshowtheinfluenceoftheseparametersonthenetworkconnectivityand,therefore,theresilienceagainstfailingnodesandcommunicationchannels.

Shortfall-basedOptimalSecurityProvisioningforInternetofThings AntoninoRullo(UniversityofCalabria),EdoardoSerra(BoiseStateUniversity),JorgeLobo(UniversitatPompeaFabra),ElisaBertino(PurdueUniversity)

WepresentaformalmethodforcomputingthebestsecurityprovisioningforInternetofThings(IoT)scenarioscharacterizedbyahighdegreeofmobility.Thesecurityinfrastructureisintendedasasecurityresourceallocationplan,computedasthesolutionofanoptimizationproblemthatminimizestheriskofhavingIoTdevicesnotmonitoredbyanyresource.Weemploytheshortfallasariskmeasure,aconceptmostlyusedintheeconomics,andadaptittoourscenario.Weshowhowtocomputeandevaluateanallocationplan,andhowsuchsecuritysolutionsaddressthecontinuoustopologychangesthataffectanIoTenvironment.

GroupDifferentialPrivacy-preservingDisclosureofMulti-levelAssociationGraphs BalajiPalanisamy(UniversityofPittsburgh),ChaoLi(UniversityofPittsburgh),PrashantKrishnamurthy(UniversityofPittsburgh)

Traditionalprivacy-preservingdatadisclosuresolutionshavefocusedonprotectingtheprivacyofindividual’sinformationwiththeassumptionthatallaggregate(statistical)informationaboutindividualsissafefordisclosure.Suchschemesfailtosupportgroupprivacywhereaggregateinformationaboutagroupofindividualsmayalsobesensitiveandusersofthepublisheddatamayhavedifferentlevelsofaccessprivilegesentitledtothem.WeproposethenotionofGroupDifferentialPrivacythatprotectssensitiveinformationofgroupsofindividualsatvariousdefinedprivacylevels,enablingdatauserstoobtainthelevelofaccessentitledtothem.Wepresentapreliminaryevaluationoftheproposednotionofgroupprivacythroughexperimentsonrealassociationgraphdatathatdemonstratetheguaranteesongroupprivacyonthediscloseddata.

TrackingInformationFlowinCyber-PhysicalSystems StefanGries(UniversityofDuisburg-Essen),MarcHesenius(UniversityofDuisburg-Essen),VolkerGruhn(UniversityofDuisburg-Essen)

Cyber-PhysicalSystemsaredistributed,heterogeneous,decentralizedandlooselycouplednetworksinwhichindividualsystemsmeasurephysicalprocesses,exchangeinformation,andinfluenceprocesses.Sensorsmeasurethesephysicalprocesses,whileaggregatorsprocessthemandactuatorsperformresultingactions.Decisionsareoftenbasedonsensordatacollectedbyothersystems.Furthermore,theaggregatorsalsointerchangeinformationandusethemtoderiveowndecisions.Decisionsmustbecomprehensible.However,thisisonlythecaseifalldatadependenciesareknown.Duetothesizeofthesenetworks,theirloosecouplingandtheirdynamicbehavior,decisionsmadebyasystemarenotalwayseasytounderstand.Ifanerroroccursinthesystem,theerrorsourcemustbeidentified.Itmustbeknownonwhichdataadecisionwasbased.However,sincethedecisioncanbebasedoninformationfromothernodes,thesearchfortheerrorsourceisnotatrivialtask.Keepinmind,thatdependentnodescanhavedependenciesthemselvesaswell.WepresenttheInformationFlowMonitor(IFM)thatcollectsinformationaboutsemanticdatadependenciesindynamicnetworks.Thecollecteddependencyinformationisprovidedatacentralnetworklocation.Subsequently,semanticdependenciesbetweeninformationcanbevisualized.

Privacy-preservingMatchmakinginGeosocialNetworkswithUntrustedServers QiuxiangDong(ArizonaStateUniversity),DijiangHuang(ArizonaStateUniversity)

AsamajorbranchofLBSs,geosocialnetworkingservicesbecomepopular.Animportantfunctionalityofgeosocialnetworkingservicesisallowingpeopletofindpotentialfriendswhohavesimilarprofilewithincloseproximityandinitiatecommunicationwitheachother.However,inordertorealizethisfunctionality,mostexistingservicesrequiremobileuserstorevealtheirprofilesandlocationinformationtoanuntrustedserviceprovider,whichmayexposeLBSstovulnerabilitiesforabuseandendangermobileusers’privacy.Toaddressthisproblem,weproposetoencryptusers’profilewithanewsearchableencryptionscheme.Combiningthissearchableencryptionschemewithothercryptographictechniquesweconstructaprivacypreservingmatchmakingsystem.Comparedwithapreviousonethataimstosolvethesameproblem,oursismoresecure,supportsmoreflexiblefunctionalitiesandmovescomputationallyheavykeyupdatestoresourcefulserviceproviders.

You’veBeenTricked!AUserStudyoftheEffectivenessofTyposquattingTechniques JeffreySpaulding(SUNYBuffalo),ShambhuUpadhyaya(SUNYBuffalo),AzizMohaisen(SUNYBuffalo)

ThenefariouspracticeofTyposquattinginvolvesdeliberatelyregisteringInternetdomainnamescontainingtypographicalerrorsthatprimarilytargetpopulardomainnames,inanefforttoredirectuserstounintendeddestinationsorstealingtrafficformonetarygain.Typosquattinghasexistedforwellovertwodecadesandcontinuestobeacrediblethreattothisday.AsrecentlyshownintheonlinemagazineSlate.com[16],cybercriminalshaveattemptedtodistributemalwarethroughNetflix.om,atyposquattedvariantofthepopularstreamingsiteNetflix.comthatusesthecountrycodetop-leveldomain(ccTLD)forOman(.om).Whilemuchofthepriorworkhasexaminedvarioustyposquattingtechniquesandhowtheychangeovertime,nonehaveconsideredhoweffectivetheyareindeceivingusers.Inthispaper,weattempttofillinthisgapbyconductingauserstudythatexposessubjectstoseveraluniformresourcelocators(URLs)inanattempttodeterminetheeffectivenessofseveraltyposquattingtechniquesthatareprevalentinthewild.WealsoattempttodetermineifthesecurityeducationandawarenessofcybercrimessuchastyposquattingwillaffectthebehaviorofInternetusers.Ultimately,wefoundthatsubjectstendtocorrectlyidentifytyposquattingwhichaddscharacterstothedomainnames,whilethemosteffectivetechniquestodeceiveusersinvolvespermutationsandsubstitutionsofcharacters.Wealsofoundthatsubjectsgenerallyperformedbetterandfasteratidentifyingtyposquatteddomainnamesafterbeingthoroughlyeducatedaboutthem,andthatcertainattributessuchasAgeandEducationaffecttheirbehaviorwhenexposedtothem.

Real-timeDetectionofIllegalFileTransfersintheCloud AdamBowers(MissouriUniversityofScienceandTechnology),DanLin(MissouriUniversityofScienceandTechnology),AnnaSquicciarini(ThePennsylvaniaStateUniversity),AliHurson(MissouriUniversityofScienceandTechnology)

Therehasbeenaprolificriseinthepopularityofcloudstorageinrecentyears.Whilecloudstorageoffersmanyadvantagessuchasflexibilityandconvenience,usersarenowunabletotellorcontroltheactuallocationsoftheirdata.Thislimitationmayaffectusers’confidenceandtrustinthestorageprovider,orevenbeunsuitableforstoringdatawithstrictlocationrequirements.Toaddressthisissue,weproposeanillegalfiletransferdetectionframeworkthatconstantlymonitors

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thereal-timefiletransfersinthecloudandiscapableofdetectingpotentialillegaltransferswhichmovessensitivedataoutsidethe(“legal”)boundariesspecifiedbythefileowner.Themainideaistoclassifyingmultipleusers’locationpreferenceswhenmakingthedatastoragearrangementinthecloudnodes.Wemodelthelegalfiletransfersamongnodesasaweightedgraphandthenmaximizetheprobabilityofstoringdataitemsofsimilarprivacypreferencesinthesameregion.ThenweleveragethesocketmonitoringfunctionsprovidedbyLAST-HDFS(arecentlocation-awareHadoopfilestoragesystem)tomonitorthereal-timecommunicationamongcloudnodes.Basedonourlegalfiletransfergraphandthedetectedcommunication,weproposeanapproachtocalculatetheprobabilityofthedetectedtransfertobeillegal.Wehaveimplementedourproposedframeworkandourexperimentalresultsindicatethatourapproachisabletodetectmuchmoreillegalfiletransfersthanthestateoftheart.

EyesoftheSwarm:Streamers'DetectioninBitTorrent DanielSilva(FluminenseFederalUniversity),AntonioRocha(FluminenseFederalUniversity)

ManyBitTorrent(BT)clientsareusingtheseBTnetworksasavideo-on-demandservice,takingadvantageofthepopularityandthelargecollectionofmediaavailable.However,transformingtheswarmsintoanon-demandmediaservicecancauseseriousdamagetotheoverallnetworkperformance.Inthispaper,weproposeamethodology,usingtheconceptsofEntropy,andpresentaSpyBitTorrentclientthatisabletoidentifypeersstreaminginaswarm.Largescalemonitoring,forrealswarms,wereperformedtodetectthepresenceofstreamers.

Poster3:CloudsandVirtualizationCluster

Loadpredictionforenergy-awareschedulingforCloudscomputingplatformsAlexandreDambreville(LRI),JoannaTomasik(CentraleSupélec),JohanneCohen(LRI-CNRS),FabienDufoulon(LRI)

WeaddressonlineschedulingforserversofCloudserviceproviders.Eachserveriscomposedofseveralvariablespeedprocessorswhosepowerfunctionisconvex.Theserversmaybebusy,idleorswitchedoff.TheobjectiveofourschedulingistominimizetheenergyconsumedbyaCloudcomputingplatform.Toachievethisgoal,wetrytoanticipatecomputingdemandsbypredictingaworkload,thenwemodifythesetofavailableserverstofitthispredictionandfinallywescheduleourjobsontheavailableservers.ToschedulejobswehavedevelopedthePOD(PredictOptimizeDispatch)algorithm.Weevaluateitsperformanceforreal-lifetracesinthepresenceofdifferenttypesofprediction.Theanalysisshowsthatourschedulingreducesenergyconsumptionconsiderably.

Learn-as-you-gowithMegh:EfficientLiveMigrationofVirtualMachines DebabrotaBasu(NationalUniversityofSingapore),XiayangWang(InstituteofParallelandDistributedSystems,ShanghaiJiaoTongUniversity),YangHong(ShanghaiJiaoTongUniversity),HaiboChen(ShanghaiJiaoTongUniversity),StephaneBressan(NationalUniversityofSingapore)

Weproposeareinforcementlearningalgorithm,Megh,forlivemigrationofvirtualmachinesthatsimultaneouslyreducesthecostofenergyconsumptionandenhancestheperformance.Meghlearnstheuncertaindynamicsofworkloadsasit-goes.Meghusesadimensionalityreductionschemetoprojectthecombinatoriallyexplosivestate-actionspacetoapolynomialdimensionalspace.TheseschemesenableMeghtobescalableandtoworkinreal-time.WeexperimentallyvalidatethatMeghismorecost-effectiveandtime-efficientthantheMadVMandMMTalgorithms.

Machine-LearningBasedPerformanceEstimationforDistributedParallelApplicationsinVirtualizedHeterogeneousClusters SeontaeKim(UNIST),NguyenPham(UNIST),WoongkiBaek(UNIST),Young-RiChoi(UNIST)

Inavirtualizedheterogeneouscluster,foradistributedparallelapplicationwhichrunsinmultiplevirtualmachines(VMs)concurrently,thereareahugenumberofpossiblewaystoplaceitsVMs.Thispaperinvestigatesaperformanceestimationtechniquefordistributedparallelapplicationsinvirtualizedheterogeneousclusters.WefirstanalyzetheeffectsofdifferentVMconfigurationsontheperformanceofvariousdistributedparallelapplications.Wethenpresentamachinelearningbasedperformancemodelforadistributedparallelapplication.Usingaheterogeneousclusterwithtwodifferenttypesofnodes,weshowthatourmachine-learningbasedmodelscanestimatetheruntimesofdistributedparallelapplicationswithmodesterrorrates.

IncrementalelasticityforNoSQLdatastores AntonisPapaioannou(ICS-FORTHandUniversityofCrete),KostasMagoutis(ICS-FORTHandUniversityofIoannina)

ElasticityactionsinNoSQLdatastoresmovelargeamountsofdataoverthenetworktotakeadvantageofnewresources.Hereweproposeincrementalelasticity,anewmechanismforschedulingdatatransferstoajoiningserver,leadingtosmootherelasticityactionswithareducedperformanceimpact.

AFrameworkforEfficientEnergySchedulingofSparkWorkloads StathisMaroulis(AthensUniversityofEconomicsandBusiness),NikosZacheilas(AthensUniversityofEconomicsandBusiness),VanaKalogeraki(AthensUniversityofEconomicsandBusiness)

NowadaysdistributedprocessingframeworkslikeApacheSparkhavebeensuccessfullyusedfortheexecutionofbigdataapplications.Despitetheirwideadoptionlittleworkhasbeendoneintermsofcontrollingtheapplications’energyconsumption.Datacenterscontributeover2%ofthetotalUSelectricusagethereforeminimizingtheenergyutilizationofSparkapplicationcanbeextremelyhelpful.SolvingthisenergyconsumptionproblemrequirestheschedulingofSparkapplicationsinanenergy-efficientway.However,theproblemischallengingaswealsohavetoconsiderapplicationperformancerequirements.Inthiswork,weprovidetheoverviewofanovelframeworkthatorchestratestheexecutionorderofSparkapplications,exploitingDVFStotunethecomputingnodesCPUfrequenciesinordertominimizetheenergyconsumptionandsatisfyapplication’sperformancerequirements.Ourearlyexperimentalresultsillustratetheworkingandbenefitsofourframework.

TowardsaCompleteVirtualDataCenterEmbeddingAlgorithmusingHybridStrategy MPGilesh(NationalInstituteofTechnologyCalicut),SDMadhuKumar(NationalInstituteofTechnologyCalicut),LillykuttyJacob(NationalInstituteofTechnologyCalicut),UmeshBellur(IndianInstituteofTechnologyBombay)

VirtualDataCenters(VDCs)areasetofvirtualmachine(VM)endpointsconnectedbyavirtualnetwork(VN)topology.WhileVMsarespecifiedbytheircapacityalongtheaxesofCPU,memory,andothermachinelevelresources,theVNischaracterizedbytheresourcesofvirtualswitches,andthebandwidthandlatencyoflinks.Today’sclouddatacenterssupportdynamicrequestsforVDCs,byusingsoftwaredefinedembeddingstrategies,thatallowthemtomeshmultipleVDCsontotheirphysicaldatacenter(PDC)networkandmachines.Overaperiodoftime,entriesandexitsofdifferentVDCscreatemultiplefragmentsofPDCresources,which

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areunusableunlessconsolidated,resultinginpooracceptancerateofVDCs.However,suchaconsolidationwouldnecessitatecostlyliveVMandvirtualswitchmigrations.AgoodstrategyshouldtradeoffthecostofmigrationsfortheacceptancerateofVDCrequests.Inthispaper,wepresentasolutiontothisVDCembeddingproblemthatachievesahigheracceptanceratebyminimizingfragmentation,comparedtoexistingstrategies,whileminimizingthemigrationsofVMsintheexistingVDCs.Experimentalresultsshowthatwecanachieveupto5%higheracceptancetoexistingsolutionswhilehaving6%fewerrateofmigrations.

FederatingConsistencyforPartition-ProneNetworks BenjaminBengfort(UniversityofMaryland),PeteKeleher(UniversityofMaryland)

Groupsofstronglyconsistentdevicescanefficientlyordereventsunderideal(datacenter)conditions,butbecomelesseffectiveindynamicandheterogeneousenvironments.Weaklyconsistentdevicesefficientlytoleratebothfaultsanddynamicconditionsbutareslowtoconvergeonasingleorderingofsystemevents.Wepropose“federatedconsistency”,whichcombinesthestrengthsofbothapproachesintoasingleprotocol.Federatedgroupsuseastronglyconsistentinnercoreofdevicestomaintainatotallyordered,fault-tolerantsequenceofevents.Acloudofweakly-consistentdevicesdisseminatesorderingsandenablesprogressdespitevaryingconnectivityandpartitions.Thoughtheconstituentsub-protocolstakedifferent(nearlyopposite)approachestoresolvingconflicts;weshowthatexpandingdistributedversionvectorswithafortecomponentallowsthemtointer-operateeffectively.Weuseadiscreteeventsimulationtoshowthatagroupoffederateddevicescanobtainthekeyadvantagesofbothapproaches.Suchsystemshavebeeninvestigatedbefore[1],[2],butourapproachtargetsmoreactive“weaknodes”inawide-areasetting.

Mitigatingnesting-agnostichypervisorpoliciesinderivativeclouds ChandraPrakash(IITBombay),Prashanth(IITBombay),PurushottamKulkarni(IITBombay),UmeshBellur(IITBombay)

ThefixedgranularityofvirtualmachinesofferedbyIaaSprovidershaspromptedtheevolutionofderivativecloudswhereresourcesarerepackagedintosmallercontainersandleasedouttypicallyinPaaSmode.Insuchasetup,containersareprovisionedwithinvirtualmachines.Suchanestedsetupresultsintwocontrolcentersfortheresourcesusedbythosecontainers—theguestOSandtheHypervisor.Thelatter’scontrolactionsareagnosticoftheapplicationexecutingwithinaVM.Thislackofvisibilitymayresultinhypervisorcontrolthathasanon-uniformeffectontheVM’snestedcontainerswhichisundesirable.Inthiswork,weproposepolicybasedcontroloftheeffectofthehypervisor’scontrolactionsamongstthecontainersnestedintheaffectedVM.

ANovelArchitectureforEfficientFogtoCloudDataManagementinSmartCities AmirSinaeepourfard(UPC),JordiGarcia(UPC),XavierMasip-Bruin(UPC),EvaMarin-Tordera(UPC)

Traditionalsmartcityresourcesmanagementrelyoncloudbasedsolutionstoprovideacentralizedandrichsetofopendata.Theadvantagesofcloudbasedframeworksaretheirubiquity,(almost)unlimitedresourcescapacity,costefficiency,aswellaselasticity.However,accessingdatafromthecloudimplieslargenetworktraffic,highdatalatencies,andhighersecurityrisks.Alternatively,fogcomputingemergesasapromisingtechnologytoabsorbtheseinconveniences.Theuseofdevicesattheedgeprovidesclosercomputingfacilities,reducesnetworktrafficandlatencies,andimprovessecurity.Wehavedefinedanewframeworkfordatamanagementinthecontextofsmartcitythroughaglobalfogtocloudmanagementarchitecture;inthispaperwepresentthedataacquisitionblock.Asafirstexperimentweestimatethenetworktrafficduringdatacollection,andcompareitwithatraditionalrealsystem.Wealsoshowtheeffectivenessofsomebasicdataaggregationtechniquesinthemodel,suchasredundantdataeliminationanddatacompression.

Networklet:ConceptandDeploymentShengZhang(NanjingUniversity),YuLiang(NanjingUniversityofPostsandTelecommunications),ZhuzhongQian(NanjingUniversity),MingjunXiao(UniversityofScienceandTechnologyofChina),JieWu(TempleUniversity),FanyuKong(AntFinancial),SangluLu(NanjingUniversity)

Intoday’sdatacenters,resourcerequestsfromtenantsareincreasinglytransformingintohybridrequeststhatmaysimultaneouslydemandIaaS,PaaS,andSaaSresources.Thispapertacklesthechallengeofmodelinganddeployinghybridtenantrequestsindatacenters,forwhichwecoin“networklet”torepresentasetofVMsthatcollaborativelyprovideaPaaSorSaaSservice.Throughextractingnetworkletsfromtenantrequestsandthussharingthembetweentenants,wecanachieveawin-winsituationfordatacenterprovidersandtenants.

OptimisticCausalConsistencyforGeo-ReplicatedKey-ValueStores KristinaSpirovska(EPFL),DiegoDidona(EPFL),WillyZwaenepoel(EPFL)

Inthispaperwepresentanewapproachtoimplementingcausalconsistencyingeo-replicateddatastores,whichwecallOptimisticCausalConsistency(OCC).Theoptimisminourapproachliesinthatupdatesfromaremotedatacenterareimmediatelymadevisibleinthelocaldatacenter,withoutcheckingiftheircausaldependencieshavebeenreceived.Serversperformthedependencycheckneededtoenforcecausalconsistencyonlyuponservingaclientoperation,ratherthanonthereceiptofareplicateddataitemasinexistingsystems.OCCexploresanoveltrade-offinthelandscapeofcausalconsistencyprotocols.ThepotentiallyblockingbehaviorofOCCmakesitvulnerabletonetworkpartitions.Becausenetworkpartitionsarerareinpractice,however,OCCchoosestotradeavailabilitytomaximizedatafreshnessandreducethecommunicationoverhead.WefurtherproposearecoverymechanismthatallowsanOCCsystemtofallbackonapessimisticprotocoltocontinueoperatingevenduringnetworkpartitions.POCCisanimplementationofOCCbasedonphysicalclocks.WeshowthatOCCimprovesdatafreshness,whileofferingcomparableorbetterperformancethanitspessimisticcounterpart.

AutomatedPerformanceEvaluationforMulti-TierCloudServiceSystemsSubjecttoMixedWorkloads XudongZhao(ShandongUniversity),LizhenCui(ShandongUniversity),JiweiHuang(BeijingUniversityofPostsandTelecommunications),ShijunLiu(ShandongUniversity),LeiLiu(ShandongUniversity),CaltonPu(GeorgiaTech)

Inmulti-tiercloudservicesystems,performanceevaluationreliesonnumerousexperimentsinordertocollectkeymetricssuchasresourcesusage.Theapproachmayresultinhighlytime-consuminginpractice.Inthispaper,weproposeanautomatedframeworkforperformancetracking,datamanagementandanalysistominimizehumaninterventioninmultitiercloudservicesystems.Theframeworksupportfine-grainedanalysisofthemixedworkloadsthroughtheDiscrete-timeMarkov-modulatedPoissonprocess(DMMPP).Ageneralmultitierapplicationistheoreticallyformulatedasaqueueingnetworktoevaluatetheperformance.TheeffectivenessofthemodelhasbeenvalidatedthroughextensiveexperimentsconductedintheRUBiSbenchmarksystem.

DecentralisedRuntimeMonitoringforAccessControlSystemsinCloudFederations

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MdSadekFerdous(UniversityofSouthampton),AndreaMargheri(UniversityofSouthampton),FedericaPaci(UniversityofSouthampton),MuYang,VladimiroSassone(UniversityofSouthampton)

Cloudfederationisanemergentcloud-computingparadigmwherepartnerorganisationssharedataandserviceshostedontheirowncloudplatforms.Inthiscontext,itiscrucialtoenforceaccesscontrolpoliciesthatsatisfydataprotectionandprivacyrequirementsofpartnerorganisations.However,duetothedistributednatureofcloudfederations,theaccesscontrolsystemalonedoesnotguaranteethatitsdeployedcomponentscannotbecircumventedwhileprocessingaccessrequests.Inordertopromoteaccountabilityandreliabilityofadistributedaccesscontrolsystem,wepresentadecentralisedruntimemonitoringarchitecturebasedonblockchaintechnology.

Poster4:DistributedSystemsandNetworkingCluster

DuoFS:AnAttemptatEnergy-SavingandRetainingReliabilityofStorageSystems ShuYin(HunanUniversity)

AsissuesoftheEnergyWallandtheReliabilityWallbecomeunavoidable,itisademandingandchallengingtasktoreduceenergyconsumptioninlarge-scalestoragesystemsinmoderndatacentreswhileretainingacceptablesystemsreliability.Mostenergyconservationtechniquesinevitablyhaveadverseimpactsontheparalleldisksystems.Toaddressthereliabilityissuesofenergy-efficientparallelstoragesystems,weproposeareliableenergy-efficientstoragesystemcalledDuoFS,whichaimsatimprovingbothenergyefficiencyandreliabilityofparallelstoragesystemsbyseamlesslyintegratingHDDsandSSDs.Withthehelpofthemiddlewarelayer,DuoFScandistributepopulardatatoSSD-basednodesandputHDD-basednodesintothelow-powermodeunderlightworkloadconditionswithoutmodificationoftheparallelsystems

AProposalofanEfficientTrafficMatrixEstimationunderPacketDrops KoheiWatabe(NagaokaUniversityofTechnology),ToruMano(NTTNetworkInnovationLaboratories),KimihiroMizutani(NTTNetworkInnovationLaboratories),OsamuAkashi(NTTNetworkInnovationLaboratories),KenjiNakagawa(NagaokaUniversityofTechnology),TakeruInoue(NTTNetworkInnovationLaboratories)

Trafficmatrix(TM)estimationhasbeenextensivelystudiedfordecades.Althoughconventionalestimationtechniquesassumethattrafficvolumesareunchangedbetweenoriginsanddestinations,packetsareoftendiscardedonapathduetotrafficburstiness,silentfailures,etc.ThispaperproposesanovelTMestimationmethodthatworkscorrectlyevenunderpacketdrops.ThemethodisestablishedonaBooleanfaultlocalizationtechnique;thetechniquerequiresfewercountersthoughitonlydetermineswhethereachlinkishealthy.ThispaperextendstheBooleantechniquesoastodealwithtrafficvolumeswitherrorboundsjustbyasmallnumberofcounters.Alongwithsubmodularoptimizationfortheminimumcounterplacement,weevaluateourmethodwithrealnetworkdatasets.

StragglerMitigationforDistributedBehavioralSimulation EmanBinKhunayn(UniversityofMelbourne),ShanikaKarunasekera(UniversityofMelbourne),HairuoXie(UniversityofMelbourne),KotagiriRamamohanarao(UniversityofMelbourne)

Runninglarge-scalebehavioralsimulationsrequireshighcomputationalpower,whichcanbeacquiredbydistributingcomputationworkloadtomultiplecomputingnodes(i.e.,workers)thatruninparallel.TheimplementationsofsuchsystemscommonlyfollowtheBulkSynchronousParallel(BSP)model.However,implementationsusingBSPusuallysufferfromthestragglerproblem,wherethedelayofanyworkerslowsdowntheentiresimulation.Theproblemusuallyoccursduetocommunicationdelaysorimbalancedworkloadamongworkers.Tomitigatethestragglerproblem,weproposeanovelparallelcomputationalmodel,calledPrioritySynchronousParallel(PSP)model.PSPexploitsdatadependenciesofparallelprocessestodeterminehighprioritydatatobecomputedandsynchronizedwhilecomputingtheremainingdata.PSPisimplementedandevaluatedusingtrafficsimulationsforthreelargecities.TheproposedtechniqueshowssignificantperformanceimprovementsovertheBSPmodel.

SupportingResourceControlforActorSystemsinAkka AhmedAbdelMoamen(UniversityofSaskatchewan),DezhongWang(UniversityofSaskatchewan),NadeemJamali(UniversityofSaskatchewan)

AlthoughtherearemodelsandprototypeimplementationsforcontrollingresourceuseinActorsystems,theyaredifficulttoimplementforproductionimplementationsofActorssuchasAkka.Thisisbecausethemessagingandschedulinginfrastructuresofruntimesystemsvarywidelyandareincreasinglycomplex.Inthispaper,wecomparetwodifferentwaysofapproximatingactor-levelcontrolsupportforAkka.Thefirstimplementationexpectsactormessagestoincludeestimatesofresourcesrequiredforprocessingthem.Thesecondimplementationsimplytracksactors’resourceusetodecidewhentheyshouldbeschedulednext.Wepresentexperimentalresultsontheperformancecostoftheseresourcecontrolmechanisms,aswellastheirimpactonresourceutilization.

ADistributedOperatingSystemNetworkStackandDeviceDriverforMulticores BMSaifAnsary(ECE,VirginiaTech),AntonioBarbalace(ECE,VirginiaTech),BinoyRavindran(ECE,VirginiaTech),ThomasLazor(ECE,VirginiaTech),Ho-RenChuang(ECE,VirginiaTech)

Withtheadvancesinnetworkspeedsasingleprocessorcannotcopeanymorewiththegrowingnumberofdatastreamsfromasinglenetworkcard.MulticoreprocessorscomeatarescuebuttraditionalSMPOSes,whichintegratethesoftwarenetworkstack,scaleonlytoacertainextent,limitinganapplication’sabilitytoservemoreconnectionswhileincreasingthenumberofcores.Ontheotherhand,kernelbypasssolutionsseemtoscalebetter,butlimitresourceflexibilityandcontrol.WeproposeattackingtheseproblemswithadistributedOSdesign,usingmultiplenetworkstacks(oneperkernel)andrelyingonmulti-queuehardwareandhardwareflowsteering.Thiscreatesasingle-socketabstractionamongkernelswhileminimizinginter-corecommunication.Weintroduceourdesign,consistingofadistributednetworkstack,adistributeddevicedriver,andaload-balancingalgorithm.Wecompareourprototype,NetPopcorn,withLinux,AffinityAccept,FastSocket.NetPopcornacceptsbetween5to8timesmoreconnectionsandreducesthetaillatencycomparedtothesecompetitors.WealsocompareNetPopcornwithmTCPandobservethatforhighcorecounts,mTCPacceptsonly18%moreconnectionsyetwithhighertaillatencythanNetPopcorn.

CachePotentialityofMONs:APrime PeiyanYuan(HenanNormalUniversity),HonghaiWu(HenanUniversityofScienceandTechnology),XiaoyanZhao(HenanNormalUniversity),ZhengnanDong(HenanNormalUniversity)

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NodebuffersizehasabiginfluenceonperformanceofMobileOpportunisticNetworks(MONs).Thisismainlybecauseeachnodeshouldtemporarilycachepacketstodealwiththeintermittentlyconnectedlinks.Inthispaper,westudyfundamentalboundsonnodebuffersizebelowwhichthenetworksystemcannotachievetheexpectedperformance.Giventheconditionthateachlinkhasthesameprobabilityptobeactive,andqtobeinactiveduringeachtimeslot,thereexitsacriticalvaluepcfromapercolationperspective.Ifp>pc,thenetworkisinthesupercriticalcase,thereisanachievableupperboundonthebuffersizeofnodes,independentoftheinactiveprobabilityq.Whenp<pc,thenetworkisinthesubcriticalcase,andthereexistsaclosed-formsolutionforbufferoccupation,whichisindependentofthesizeofthenetwork.

Oak:User-TargetedWebPerformance MarcelFlores(NorthwesternUniversity),AlexanderWenzel(NorthwesternUniversity),AleksandarKuzmanovic(NorthwesternUniversity)

Webperformancehaslongprovedtobeoneofthemostsoughtafteranddifficulttoachievecomponentsfortheweb.Sincetheinceptionofthemodernwebinfrastructure,thesituationhasbeengrowingincomplexity,addingremotehostsandobjects,providingeverythingfromcomputationinfrastructure,contentdistributioncapability,andtargetedadvertising.Whilemanyofthesecomponentsprovideimprovementsforsomeusers,thecomplexityoftheInternetoftenleavesotheruserssufferingfrompoorperformance.WeproposeOak,asystemwhichaddressesclientperformanceontheindividuallevel,henceaddressingchallengeswhichmaybeuniquetotheuser.Oakmeasuresauser’sperformanceforobjectsloadingonapage,anddetermineswhichcomponentsareunderperforming.Oakfurtherprovidesanautomatedmechanismbywhichsitesareabletoreplaceresourceswiththoseprovidedbyabetterperformingalternativeserviceforaparticularuser.Inthiswork,wedemonstratetheprevalenceofunder-performingservicesontheweb,findingthatover60%oftheAlexaTop500haveatleastoneunder-preformingserver.WefurtherevaluateOakonexperimentalandpopularexistingwebpages,anddemonstrateitseffectivenessinmakingdecisionsinexistingenvironmentsandwithadistributeduserbase.

Ctrl-A:ASelf-*DistributedandIn-bandSDNControlPlaneMarcoCanini(UniversitécatholiquedeLouvain),IosifSalem(ChalmersUniversityofTechnology),LironSchiff(TelAvivUniversity),EladMichaelSchiller(ChalmersUniversityofTechnology),StefanSchmid(AalborgUniversity&TUBerlin)

Adoptingdistributedcontrolplanesiscriticaltowardsensuringhighavailabilityandfault-toleranceofdependableSoftware-DefinedNetworks(SDNs).However,designingandbootstrappingadistributedSDNcontrolplaneisachallengingtask,especiallyiftobedonein-band,withoutadedicatedcontrolnetwork,andwithoutrelyingonlegacynetworkingprotocols.Oneofthemostappealingandpowerfulnotionsoffault-toleranceisself-organizationandthispaperdiscussesthepossibilityofselforganizingalgorithmsforin-bandcontrolplanes.

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Tutorial Abstracts

Tutorial1:ServerlessProgramming(FunctionasaService)PaulCastro(IBMT.J.WatsonResearchCenter),VatcheIshakian(BentleyUniversity),VinodMuthusamy(IBMT.J.WatsonResearchCenter),AleksanderSlominski(IBMT.J.WatsonResearchCenter)

ServerlessComputing(FunctionasaService)isemergingasanewandcompellingparadigmforthedeploymentofcloudapplications,largelyduetotherecentshiftofenterpriseapplicationarchitecturestocontainersandmicroservices.

FromtheperspectiveofanInfrastructure-as-a-Service(IaaS)customer,thisparadigmshiftpresentsbothanopportunityandarisk.Ontheonehand,itprovidesdeveloperswithasimplifiedprogrammingmodelforcreatingcloudapplicationsthatabstractsawaymost,ifnotall,operationalconcerns;itlowersthecostofdeployingcloudcodebychargingforexecutiontimeratherthanresourceallocation;anditisaplatformforrapidlydeployingsmallpiecesofcloudnativecodethatrespondstoevents,forinstance,tocoordinatemicroservicecompositionsthatwouldotherwiserunontheclientorondedicatedmiddleware.Ontheotherhand,deployingsuchapplicationsinaserverlessplatformischallengingandrequiresrelinquishingtotheplatformdesigndecisionsthatconcern,amongotherthings,quality-of-service(QoS)monitoring,scaling,andfault-toleranceschemes.

Fromtheperspectiveofacloudprovider,serverlesscomputingprovidesanadditionalopportunitytocontroltheentiredevelopmentstack,reduceoperationalcostsbye_cientoptimizationandmanagementofcloudresources,andenablingaserverlessecosystemthatencouragesthedeploymentofadditionalcloudservices.

Serverlessplatformspromisenewcapabilitiesthatmakewritingscalablemicroserviceseasierandcosteffective,positioningthemselvesasthenextstepintheevolutionofcloudcomputingarchitectures.MostoftheprominentcloudcomputingprovidersincludingAmazon,IBM,Microsoft,andGooglehaverecentlyreleasedserverlesscomputingcapabilities.Therearealsoseveralopen-sourceeffortsincludingtheOpenLambdaproject.

Inthistutorial,wewillpresentserverlesscomputing,surveyexistingserverlessplatformsfromindustry,academia,andopensourceprojects,identifykeycharacteristicsandusecases,anddescribetechnicalchallengesandopenproblems.Ourtutorialwillinvolveahands-onexperienceofusingtheserverlesstechnologiesavailablefromdifferentcloudproviders(e.g.IBM,Amazon,GoogleandMicrosoft).Weexpectouruserstohavebasicknowledgeofprogrammingandbasicknowledgeofcloudcomputing.

PaulCastro,Ph.D.isaResearchStaffMemberattheIBMWatsonResearchCenter.Hehasbeenactiveinresearchonmobileandpervasivecomputing,cloudinfrastructure,wirelesslocationsystems,locationdatabases,streamprocessing,andenterprisewebapplicationsandhasbeenawardedseveralpatentsintheseareas.Hehasworkedoncloudservicesforsupportingmobileapplicationsrunningonvarioussmartphoneplatforms.Workfromhisresearchintheareaofmulti-deviceapplicationsupportwasrecentlyreleasedaspartoftheIBMBluemixMobileBackendasaService.HehasearnedtwoIBMTechnicalAchievmentAwardsfortheIBMSmartCloudWebMeetingsformobileclientsandtheIntelligentNotificationSystem.Mostrecently,heworkedonIBMOpenWhiskforBluemix,withafocusonmobilesolutions.

VatcheIshakianisanAssistantProfessorintheComputerInformationSystemsdepartmentatBentleyUniversity,beforestartinghisacademiccareer,VatchewasaResearchStaffMemberatIBMResearchworkingonseveralprojectsincludingIBMOpenWhiskserverlesscomputingplatform.VatcheComputerhisPhDinComputerSciencefromBostonUniversity.Hisresearchinterestsincludedistributedbusinessprocessmanagement,Servicescomposition,andpricedbasedmodelsforcloudservices.

VinodMuthusamyisaResearchStaffMemberintheComponentSystemsGroupattheIBMT.J.WatsonResearchCenter.HecompletedhisPhDinComputerEngineeringattheUniversityofToronto.Vinod’sresearchinterestsincludepublish/subscribeeventprocessing,anddistributedbusinessprocessmanagement.Mostrecently,heworkedonIBMOpenWhiskServerlessComputingplatform.

AleksanderSlominskiisaResearchStaffMemberintheServicesandAPIEcosystemsGroupattheIBMT.J.WatsonResearchCenter.HeisinterestedindevelopmentofapplicationsforforfutureAPIEconomythattakeadvantageofupcomingcloudprogrammingapproaches,suchasserverlesscomputings,forcompositionsandorchestrationofcomponentsintobusinessworkflows.Mostrecently,heworkedonIBMIBMOpenWhiskServerlessComputingplatform.

Tutorial2:SensorCloud:ACloudofSensorNetworksSanjayMadria(MissouriUniversityofScienceandTechnology)

Traditionalmodelofcomputingwithwirelesssensorsimposesrestrictionsonhowefficientlywirelesssensorscanbeusedduetoresourceconstraints.NewermodelsforinteractingwithwirelesssensorssuchasInternetofThingsandSensorCloudaimtoovercometheserestrictions.Inthistutorial,Iwilldiscusssensorcloudarchitectures,whichenabledifferentwirelesssensornetworks,spreadinahugegeographicalareatoconnecttogetherandbeusedbymultipleusersatthesametimeondemandbasis.Iwillfurtherdiscusshowvirtualsensorsassistincreatingamultiuserenvironmentontopofresourceconstrainedphysicalwirelesssensorsandcanhelpinsupportingmultipleapplicationson-demandbasis.Iwillthenpresentsomesecurityissuesandprovideoverviewofthesolutionstotheproblemsfromtheliterature.Inparticular,Iwilldiscussenergyefficientprivacyanddataintegritypreservingdataaggregationalgorithm,riskassessmentinsensorcloudaswellasattribute-basedaccesscontrolforsensorcloudapplications.Thetopicscoveredwillbe:1.CloudofSensors–SensorCloudArchitectures2.VirtualizationinSensorCloud3.SchedulingandQoSinSensorCloud4.DatacompressionandSecureAggregationinSensorCloud5.Security,PrivacyandRiskIssuesinSensorCloud

SanjayKMadriareceivedhisPh.D.inComputerSciencefromIndianInstituteofTechnology,Delhi,Indiain1995.HeisafullprofessorintheDepartmentofComputerScienceattheMissouriUniversityofScienceandTechnology(formerly,UniversityofMissouri-Rolla,USA).Hehaspublishedover235Journalandconferencepapersintheareasofmobileandsensorcomputing,cloudandcybersecurity.HewonfiveIEEEbestpapersawardsinconferencessuchasIEEEMDM2011,IEEEMDM2012andIEEESRDS2015.Heisaco-authorofabookpublishedbySpringerinNov2003.Hehaspresentedtutorialsintheareasofsecuresensorcloud,cloudcomputing,mobilecomputing,etc.NSF,NIST,ARL,ARO,AFRL,DOE,Boeing,HangsoftandBoeinghavefundedhisresearchprojects,amongothers.HewasawardedJSPS(JapaneseSocietyforPromotionofScience)visitingscientistfellowshipin2006andASEE(AmericanSocietyofEngineeringEducation)fellowshipfrom2008to2017.In2012,hewasawardedNRCFellowshipbyNationalAcademies.Hereceivedfacultyexcellenceandresearchawardsintheyears2007,2009,2011,2013and2015fromhisuniversityforexcellenceinresearch.HeisACMDistinguishedScientist,andACMDistinguishedSpeakerandIEEESeniorMemberaswellasIEEEGoldenCoreAwardee.

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ADSN 2017 Workshop Abstracts

UnderstandingandImprovingTemporalFairnessonanElectronicTradingVenueHaydenMelton(DeakinUniversity)

Fairness,ingeneral,isatopicthathasreceivedmuchattentioninresearchondistributedsystems.Intheirapplicationaselectronictradingvenues,however,temporalfairnessremainsatopicthatispoorlyunderstood.Thisisconcerningbecauseoperatorsofthesevenuesgenerallyhaveobligationstoensuretheirfairness.Consequently,thispaper(1)describeswhattemporalfairnessisandisnot,(2)identifiesthingsthatcanmakeitelusive,and(3)describesamechanismforimprovingitthatwasrecentlyretrofittedtoamajorFXtradingvenue:ThomsonReutersMatching.

CertificateLessCryptography-basedRuleManagementProtocolforAdvancedMissionDeliveryNetworksJonghoWon(PurdueUniversity),AnkushSingla(PurdueUniversity),ElisaBertino(PurdueUniversity)

AssuredMissionDeliveryNetwork(AMDN)isacollaborativenetworktosupportdata-intensivescientificcollaborationsinamulti-cloudenvironment.Eachscientificcollaborationgroup,calledamission,specifiesasetofrulestohandlecomputingandnetworkresources.SecurityisanintegralpartoftheAMDNdesignsincetherulesmustbesetbyauthorizedusersandthedatageneratedbyeachmissionmaybeprivacy-sensitive.Inthispaper,weproposeaCertificateLesscryptography-basedRule-managementProtocol(CL-RP)forAMDN,whichsupportsauthenticatedruleregistrationsandupdateswithnon-repudiation.WeevaluateCL-RPthroughtest-bedexperimentsandcompareitwithotherstandardprotocols.

FaultySensorDataDetectioninWirelessSensorNetworksUsingLogisticalRegressionTianyuZhang(UniversityofHyogo),QianZhao(UniversityofHyogo),YukikazuNakamoto(UniversityofHyogo)

Wirelesssensornetworks(WSNs)arecommonlyusedtomonitorchangesinanenvironmentandpreventdisasterssuchasstructuralinstability,forestfires,andtsunamis.WSNsshouldrapidlyrespondtochangesandmustprocessandanalyzesensordatainadistributedwaytominimizebatteryconsumption.Ontheotherhand,machinelearning(ML)algorithmsisapowerfultoolfordataanalyzing.However,MLalgorithmsaresocomplexthatcannotbeexecutedonresourceconstrainedsensornodes.AnotherchallengeofusingMLalgorithmsinWSNsisthatMLalgorithmsaredifficulttobedistributedoneverysensornode.BecauseMLalgorithmsarebasedonstatistics'methodsthatneedcollectingamountofdatatoapproachaccuracy.Inthispaper,weproposeamethodthatdividesalogisticalregressionMLmethodintotwosteps,thendistributesthetwostepsontosinknodeandsensornodestodetectfaultysensordata.

AnAdaptability-EnhancedRoutingMethodforMultipleGateway-basedWirelessSensorNetworksUsingSecureDispersedDataTransferRyumaTani(HiroshimaCityUniversity),KentoAoi(HiroshimaCityUniversity),EitaroKohno(HiroshimaCityUniversity),YoshiakiKakuda(HiroshimaCityUniversity)

Inconventionalwirelesssensornetworks(hereinafterreferredtoasWSNs),thesinglesinknodemodelhasbeenemployedtocollectandstorethemeasureddatatoprovidetotheexternalusersofWSNs.However,thesinglesinkmodelofWSNscanbethesinglepointoffailureforsomeusage.Tocounterthisproblem,wecanemploymultiplegateway-basedWSNs.Inaddition,WSNsaresusceptibletovariouskindsofattackssuchaseavesdropping.Tocountereavesdropping,wealreadyhaveproposedthesecretsharingscheme-basedsecuredisperseddatatransfermethod(hereinafterreferredtoasthesecuredisperseddatatransfermethod).Whilewehadconfirmedthatthesecuredisperseddatatransfermethodiseffectivetocountereavesdroppingthroughtheuseofradioareadisjointmultiplepaths,wealsofoundthatthesecuredisperseddatatransfermethodcannotbeeffectiveinsevereenvironmentssuchasinanetworkwithalowdensityofnodes.

AProgressiveDownloadMethodBasedonTimer-DrivenRequestingSchemesUsingMultipleTCPFlowsonMultiplePathsHiroakiHoriba(HiroshimaCityUniversity),TokumasaHiraoka(HiroshimaCityUniversity),JunichiFunasaka(HiroshimaCityUniversity)

Duetothewidespreaduseofbroadbandcommunicationmedia,theconventionalTCPcannotfullyutilizesuchbroadbandwidth,somanyimprovementsonTCPitselfandalotofacceleratingmethodswhichusemultipleTCPflowshavebeenproposed.Inaddition,videohostingservicesontheInternetasanewmediumhavebecomepopular,andprogressivedownloadingmethods,whichdownloadsegmentedvideodatawhilereplayingthem,areadoptedonvarioussites.TheplaybackqualityofprogressivedownloadmethodshasbeenimprovedbytheexistingmethodwhichestablishesmultipleTCPflowsoneachofmultiplepaths.However,theexistingmethodassumesthatbandwidth,delay,andpacketlossrateofeachpathareknown.Therefore,inthispaper,amethodusingthetimer-drivenrequestingschemewhichistobeeffectiveevenwhenbandwidth,delay,andpacketlossratearenotgiven.Moreover,itfeaturesduplicaterequestingschemetocopewithqualitydeteriorationinvideoplaybackduetoout-of-orderblockarrivalswhenapplyingprogressivedownloadusingmultiplepaths.Thispaperevaluatestheproposedmethodcomparingwiththeexistingmethodbysimulation.Asaresult,itisfoundthattheproposedmethodyieldshighperformanceenoughtokeepthevideoqualityhigherthantheexistingmethodeventhoughthenetworkconditionisnotclarifiedinadvance.Theproposalcanberegardedasanassurancenetworktechnologysinceitcanadapttothecurrentnetworkstatusandkeeptheplaybackratehigh.

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BGP 2017 Workshop Abstracts

WolfPath:Acceleratingiterativetraversing-basedgraphprocessingalgorithmsonGPUHuanzhouZhu(UniversityofWarwick),LigangHe(UniversityofWarwick)

Thereisthesignificantinterestnowadaysindevelopingtheframeworksofparallelizingtheprocessingforthelargegraphssuchassocialnetworks,Webgraphs,etc.Mostparallelgraphprocessingframeworksemployiterativeprocessingmodel.However,bybenchmarkingthestate-of-artGPU-basedgraphprocessingframeworks,weobservedthattheperformanceofiterativetraversing-basedgraphalgorithms(suchasBreadFirstSearch,SingleSourceShortestPathandsoon)onGPUislimitedbythefrequentdataexchangebetweenhostandGPU.Inordertotackletheproblem,wedevelopaGPU-basedgraphframeworkcalledWolfPathtoacceleratetheprocessingofiterativetraversing-basedgraphprocessingalgorithms.InWolfPath,theiterativeprocessisguidedbythegraphdiametertoeliminatethefrequentdataexchangebetweenhostandGPU.Toaccomplishthisgoal,WolfPathproposesadatastructurecalledLayeredEdgelisttorepresentthegraph,fromwhichthegraphdiameterisknownbeforethestartofgraphprocessing.InordertoenhancetheapplicabilityofourWolfPathframework,agraphpreprocessingalgorithmisalsodevelopedinthisworktoconvertanygraphintotheformatoftheLayeredEdgelist.WeconductedextensiveexperimentstoverifytheeffectivenessofWolfPath.TheexperimentalresultsshowthatWolfPathachievessignificantspeedupoverthestate-of-artGPU-basedin-memoryandout-of-memorygraphprocessingframeworks.

ANovelAuction-basedQueryPricingSchemaXingwangWang(JilinUniversity),XiaohuiWei(JilinUniversity),ShangGao(JilinUniversity),YuanyuanLiu(JilinUniversity),ZongpengLi(UniversityofCalgary)

Asacommonprocessingmethod,queryiswidelyusedinmanyareas,suchasgraphprocessing,machinelearning,statistics,etc.However,queriesareusuallypricedaccordingtovendor-specifiedfixedviews(API)ornumberoftransactions,whichignorestheheterogeneityofqueries(computingresourceconsumptionforqueryandinformationthattheanswerbrings)andviolatesthemonotoneprinciple.

Inthisworkwestudytherelationalquerypricingproblembytakingbothinformation(i.e.,data)valueandqueryresourceconsumptionsintoaccount.Wedesignefficientauctionsforquerypricing.Differentfromtheexistingquerypricingschemas,queryauctiondeterminesdatapricesthatreflectthedemand-supplyofsharedcomputingresourcesandinformationvalue(i.e.,pricediscovery).Wetargetqueryauctionthatrunsinpolynomialtimeandachievesnear-optimalsocialwelfarewithagoodapproximationratio,whileelicitstruthfulbidsfromconsumers.Towardsthesegoals,weadaptthepostedpricingframeworkingame-theoreticperspectivebycastingthequeryauctiondesignintoanIntegerLinearProgrammingproblem,anddesignaprimal-dualalgorithmtoapproximatetheNP-hardoptimizationproblem.Theoreticalanalysisandempiricalstudiesdrivenbyreal-worlddatamarketbenchmarkverifytheefficiencyofourqueryauctionschema.

Asacommonprocessingmethod,queryiswidelyusedinmanyareas,suchasgraphprocessing,machinelearning,statistics,etc.However,queriesareusuallypricedaccordingtovendor-specifiedfixedviews(API)ornumberoftransactions,whichignorestheheterogeneityofqueries(computingresourceconsumptionforqueryandinformationthattheanswerbrings)andviolatesthemonotoneprinciple.

Inthisworkwestudytherelationalquerypricingproblembytakingbothinformation(i.e.,data)valueandqueryresourceconsumptionsintoaccount.Wedesignefficientauctionsforquerypricing.Differentfromtheexistingquerypricingschemas,queryauctiondeterminesdatapricesthatreflectthedemand-supplyofsharedcomputingresourcesandinformationvalue(i.e.,pricediscovery).Wetargetqueryauctionthatrunsinpolynomialtimeandachievesnear-optimalsocialwelfarewithagoodapproximationratio,whileelicitstruthfulbidsfromconsumers.Towardsthesegoals,weadaptthepostedpricingframeworkingame-theoreticperspectivebycastingthequeryauctiondesignintoanIntegerLinearProgrammingproblem,anddesignaprimal-dualalgorithmtoapproximatetheNP-hardoptimizationproblem.Theoreticalanalysisandempiricalstudiesdrivenbyreal-worlddatamarketbenchmarkverifytheefficiencyofourqueryauctionschema.

BlockGraphChi:EnablingBlockUpdateinOut-of-coreGraphProcessingZhiyuanShao(HuazhongUniversityofScienceandTechnology),ZhenjieMei(HuazhongUniversityofScienceandTechnology),XiaofengDing(HuazhongUniversityofScienceandTechnology),HaiJin(HuazhongUniversityofScienceandTechnology)

Inthepastseveralyears,lotsofout-of-coregraphprocessingsystemsarebuilttoprocessbiggraphdatasetsincomputersystemswithlimitedmainmemory.Duetotheiterativenatureofgraphalgorithms,mostofthesesystemsemploysynchronousexecutionmodeltoorganizethecomputation,i.e.,dividethecomputingintomultiplerounds,eachofwhichcorrespondstooneiterationofthegraphalgorithm.Inordertofullyutilizethediskbandwidth,thesesystemssequentiallyscanthewholegraphdatasetateachiteration.However,asthegraphdatasetunderprocessingmaybehuge,moreiterationsgenerallymeanslargerI/Ooverheads.Althoughasynchronousimplementationofthesynchronousexecutionmodelallowsmessagepassingwithinaniteration,theeffectivenessisstilllimited.Sinceinsuchmodel,atmostonemessageisallowedtobepassedfromonevertextoanother.

Inthispaper,weinvestigatetheideaofblockupdatinginthesynchronousexecutionmodelframeworkintheout-of-coregraphprocessingsystems.Withthisnewmodel,thesystemconductsgraphalgorithmontheloadedsubgraph(i.e.,block)toitslocalconvergence,andthenswitchestoothersubgraphstocontinuethisprocess,untiltheglobalconvergenceisreached.WeimplementthisnewmodelinGraphChi(theresultsystemiscalledBlockGraphChi),andproposeagraphpartitionmethod,namedasDMLP,tocooperatewiththisnewmodel.Bythisstudy,wefoundthatcomparedwiththeoriginalexecutionmodelofGraphChi:1)thenewmodelcangenerallyreducetheamountofiterations(andthustheI/Ooverheads)forgraphalgorithms,whiletheextentofreductiondependsonthemethodofgraphpartitioningandthepropertiesofthealgorithms;2)thenewmodelcandramaticallyreducetheoverallexecutiontimeofgraphtraversalalgorithms(byupto31.4x),andbetterpartitioningmethodleadstobetterperformance;3)thenewmodelhasmuchsmallereffectivenessonimprovingtheoverallperformanceoffix-pointalgorithms,suchasPageRank,duetotheincreasedcomputationaloverhead.

IncrementalParallelComputingusingTransactionalModelinLarge-scaleDynamicGraphStructuresAnandTripathi(UniversityofMinnesota,Minneapolis),RahulR.Sharma(UniversityofMinnesota),ManuKhandelwal(UniversityofMinnesota),TanmayMehta(UniversityofMinnesota),VarunPandey(UniversityofMinnesota)

Manygraphanalyticsproblemsbenefitfromtheuseofparallelcomputingtechniquestoreducetheexecutiontime,whichcanstillbequitehighforlargegraphproblems.Thegoalofourworkistoeliminatetheneedofre-executingananalyticsprogramwhenthegraphstructureismodifiedwithasmallsetofupdatesaftertheinitialexecutionoftheprogram.Towardsthisgoal,wepresentheretheresultsofourinvestigationofincrementalcomputationtechniquesindynamicgraph

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structuresusingatransactionalmodelofparallelprogramming.Inthismodel,computationtasksinananalyticsapplicationareexecutedinparallelasserializabletransactions.Thispaperdescribeshowincrementalcomputationtechniquesaresupportedbythismodelfordynamicgraphstructures.Weusetheproblemsoffindingconnected-componentsinagraphandthegraphcoloringproblemtoillustrateourapproachforincrementalcomputations.Usingexperimentalevaluations,weshowthebenefitsofthisapproach.

AgainstSigned-GraphDeanonymizationAttacks:PrivacyProtectionforSocialNetworksJianliangGao(CentralSouthUniversity),YuLiu(CentralSouthUniversity),PingZhong(CentralSouthUniversity),JianxinWang(CentralSouthUniversity)

Socialnetworksareusuallypresentedasgraphs.Butthetopologicalcharacteristicsofgraphscouldbeusedbyattackerstodeanonymizetargetentitiesinsocialnetworks.Existingworksmostlyhaveanassumptionthatattackerknowsonlythetargetentities'neighborhoodgraph.Thisassumptionmightresultinprivacyleakagebecauseoftheignoranceoflinkpropertybetweenentities.Inrealapplications,attackersmightre-identifyentitiesinsocialnetworksbasedonnotonlythelinksbetweenentities,butalsothepropertyoflinks.Inthispaper,wetakethepropertyoflinksintoconsiderationforthefirsttimewhenachieving$k$-anonymityforsocialnetworks,whichmeanstheattackerscannotre-identifyatargetwithconfidencehigherthan$1/k$.Thelinksarecatalogedaspositiveandnegative,whichiscalledsignedgraph.Inthisbackground,weproposea$k$-anonymizationschemetoprotecttheprivacyofkeyentitiesinsocialnetworks.Theproposedschememinimizestheamountofmodificationonoriginalgraphs,whichpreservestheutilityoftheoriginaldata.Extensiveexperimentsonrealdatasetsandsyntheticgraphillustratetheeffectivenessoftheproposedscheme.Theutilityofanonymizednetworksareremainedbydemonstratingwiththeresultsofvertexdegree,betweenness,closenessandtheirKolmogorov-Smirnov(K-S)test.

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CCN-CPS 2017 Workshop Abstracts

PoliciesGuidingCohesiveInteractionsamongInternetofThingswithCommunicationCloudandSocialNetworksHenryHexmoor(SouthernIllinoisUniversity)

CohesiveinteractionamongInternetofthingnodeswillbenefitfromformationofadhoccommunicationnetworkcloudsforrapidexchangeofinformationthatispertinentfortheirsuccessfulinteraction.Longenduringinteractionsamongsuchnodeswillbenefitfromadhocsociallylinkednetworksforcollaborationonsharedobjectives.Wepresentguidelinesforformingandusingtheseconstructsandpoliciesthatconstrainthemtorequirementsofspecificapplications.

EnhancedSecurityofBuildingAutomationSystemsThroughMicrokernel-BasedControllerPlatformsXiaolongWang(UniversityofSouthFlorida),RichardHabeeb(UniversityofSouthFlorida),XinmingOu(UniversityofSouthFlorida),SiddharthAmaravadi(KansasStateUniversity),JohnHatcliff(KansasStateUniversity),MasaakiMizuno(KansasStateUniversity),MitchellLNeilsen(KansasStateUniversity),RajRajagopalan(Honeywell),SrivatsanVaradarajan(HoneywellAerospaceAdvancedTechnologyLabs)

ABuildingAutomationSystem(BAS)isacomplexdistributedCyber-PhysicalSystemthatcontrolsbuildingfunctionalitiessuchasheating,ventilation,andaircondition-ing(HVAC),lighting,access,emergencycontrol,andsoon.ThereisagrowingopportunityandmotivationforBAStobeintegratedintoenterpriseITnetworkstogetherwithvariousnew""smart""technologiestoimproveoccupantcomfortandreduceenergyconsumption.Thesenewtechnologiescoexistwithlegacyapplications,creatingamixed-criticalityenvironment.Inthisenvironment,assystemsareintegratedintoITnetworks,newattackvectorsareintroduced.Thus,networkednon-criticalapplicationsrunningontheOSplatformmaybecompromised,leavingthecontrolsystemsvulnerable.Theindustryneedsareliablecomputingfoundationthatcanprotectandisolatetheseendangeredcriticalsystemsfromuntrustedapplications.

Thisworkpresentsanovelkernel-basedapproachtosecurecriticalapplications.Ourmethodusesasecurity-enhanced,microkernelarchitecturetoensurethesecurityandsafetypropertiesofBASinapotentiallyhostilecyberenvironment.WecomparethreesystemdesignandimplementationsforasimpleBASscenario:1)usingthemicrokernelMINIX3enhancedwithmandatoryaccesscontrolforinter-processcommunication(IPC),2)usingseL4,aformallyverified,capability-basedmicrokernel,and3)usingLinux,amonolithickernelOS.Weshowthroughexperimentthatwhenthenon-criticalapplicationsarecompromisedinbothMINIX3andseL4,thecriticalprocessesthatimpactthephysicalworldarenotaffected.WhereasinLinux,thecompromisedapplicationscaneasilydisruptthephysicalprocesses,jeopardizingthesafetypropertiesinthephysicalworld.ThisshowsthatmicrokernelsareasuperiorplatformforBASorothersimilarcontrolenvironmentsfromasecuritypointofview,anddemonstratesthroughexamplehowtoleveragethearchitecturetobuildarobustandresilientsystemforBAS.

HighlevelDesignofaHomeAutonomousSystemBasedonCyberPhysicalSystemModelingBasmanAlhafidh(FloridaInstituteofTechnology),WilliamH.Allen(FloridaInstituteofTechnology)

Theprocessusedtobuildanautonomoussmarthomesystemusingcyber-physicalsystems(CPS)principleshasreceivedmuchattentionbyresearchersanddevelopers.However,therearemanychallengesduringthedesignandimplementationofsuchasystem,suchasPortability,Timing,Prediction,andIntegrity.Thispaperpresentsanovelmodelingmethodologyforasmarthomesysteminthescopeofcyber-physicalinterfacethatattemptstoovercometheseissues.Wediscussahigh-leveldesignapproachthatsimulatesthefirstthreelevelsofa5CarchitectureinCPSlayersinasmarthomeenvironment.Adetaileddescriptionofthemodeldesign,architecture,andasoftwareimplementationviaNetLogosimulationprogramwillbepresented.Ourdesignprovidesanexamplefordevelopersonhowtoimplementanecosysteminahomeenvironmentaspartofasmartcities'infrastructurebasedonCPSdesignprinciples.

ACyberPhysicalBuses-and-DronesMobileEdgeInfrastructureforLargeScaleDisasterEmergencyCommunicationsMamtaNarang(AucklandUniversityofTechnology),WilliamLiu(AucklandUniversityofTechnology),JairoAGutierrez(AucklandUniversityofTechnology),LucaChiaraviglio(UniversityofRomeTorVergata)

Immediatelyafteradisaster,thenormaltelecommunicationinfrastructure,includingwiredandwirelessnetworks,isoftenseriouslycompromisedandcannotguaranteeregularcoverageandreliablecommunicationsservices.Thesetemporarily-missingcommunicationscapabilitiesarecrucialtorescuersandaffectedcitizensastherespondersneedtoeffectivelycoordinateandcommunicatetominimizethelossoflivesandproperty.Acyber-physicalsystem(CPS)iscomposedofintegratedcommunication,computationandphysicalobjects,andcyber-physicalvehiclesystems(CPVSs)areanemergingfieldduetotherapidadvancementsonreal-timecomputing,mobilecommunicationsandautonomouscontrolinintelligenttransportsystems.Inthispaper,weproposeacyber-physicalbuses-and-dronesmobiLeedgeinfrastructure(AidLife)fordisasteremergencycommunications,whichaimsatarapidlydeployableresilientsystemcapableofsupportingflexiblecommunicationstoservelarge-scaledisastersituationsbyutilizingtheexistingpublictransportsystem.Inparticularweenvisionaproposalwherepublicbusescanberecruitedtotemporarilyhostportablebasestation(BS)andcomputationunitsaswellaspowerresourcessoastoformabuses-basedmobileedgeinfrastructure,andalsoaccommodatedronestoextendtheircoveragetohard-to-reachareas.OurpreliminaryresultsshowthattheAidLifesystemcanguaranteeagoodcoveragetousers,evenwhenalargenumberofnormalBSsthataredamaged.

APerformanceComparisonofContainersandVirtualMachinesinWorkloadMigrationContextKumarGaurav(VMwareSoftwareIndiaPvtLtd),PavanKarkun(VMwareSoftwareIndiaPvtLTD),Y.C.Tay(NationalUniversityofSingapore)

Thispapergivesamathematicalframeworkfordecisionmakingaroundplacingandmigratingworkloadsinadata-centerwhereapplicationsarepackagedasOScontainersrunningonvirtualmachines.ThedecisionpointonVMmigrationvscontainerkill/restart,VMforkvscontainerspawnarestudiedhere.Weproposeamathematicalmodelforthemigrationofworkloadsaforementionedcasesandalsoforsharedmemorydecayincaseofforkingavirtualmachine.Experimentalresultsareanalyzedtodeterminethevalidityofthemodel.

TowardsService-OrientedMiddlewareforCyberPhysicalSystemsNaderMohamed(MiddlewareTechnologiesLab.),SanjaLazarova-Molnar(UniversityofSouthernDenmark)

Cyber-PhysicalSystems(CPS)providemanysmartfeaturesforenhancingphysicalsystemsandenvironments.Theyaredesignedwithasetofdistributedhardware,software,andnetworkcomponentsthatareembeddedinphysicalsystemsandenvironmentsorattachedtohumans.ManyCPSatdifferentscalesarebeingdevelopedforavarietyofapplicationsthatprovidevaluableinteractionsbetweenthecyberworldandthephysicalsystemsandenvironments.However,thesedevelopmentsfacemanychallengesduetothecomplexityoftheseapplications.Anappropriatemiddlewareisneededtoprovideinfrastructuralsupportandassist

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thedevelopmentandoperationsofdiverseCPSapplications.Thispaperstudiesutilizingtheservice-orientedmiddlewareapproachforCPSanddiscussestheadvantagesandrequirementsforsuchutilization.Inaddition,itproposesaservice-orientedmiddlewareforCPS,calledCPSWare.ThismiddlewareviewsallCPScomponentsasasetofservicesandprovidesaninfrastructuretodevelopandoperateCPSapplications.ThisapproachprovidessystemicsolutionsforsolvingmanycomputingandnetworkingissuesinCPS.ItalsoenablestheintegrationofCPSwithothersystemssuchasCloudComputingandFogComputing.Inaddition,asCPScanbedevelopedforvariousapplicationsatdifferentscales,thispaperprovidesaclassificationforCPSapplicationsanddiscusseshowCPSWarecaneffectivelydealwiththesecategories.

NetworkingandCommunicationinCyberPhysicalSystemsImadJawhar(UAEUniversity),JameelaAl-Jaroodi(RobertMorrisUniversity)

Cyber-physicalsystems(CPSs)areemergingasanewtechnology,whichisusedtoprovideseamlessinteractionbetweenthephysicalandcyberworlds.Thisnovelparadigmisanaturalevolutionandextensionofwirelesssensornetworks(WSNs)andcontrolmodelstoallowforeffectivemonitoringandcontrolofphysicalsystemsfromthecomputingenvironment.Inordertosupportthisinterfaceandallowsuchsmoothinteractions,efficientnetworkingandcommunicationbetweenthephysicalandcyberworldstakeaveryimportantandcriticalrole.Inthispaper,weidentifythevariousapplicationsandcategoriesofCPSsystems,andcharacterizetheassociateddatatrafficthatisgenerated.Wealsodiscussthedifferentprotocolsandrequirementsthatareneededatthevariousnetworkinglayersfortheseapplications.Subsequently,weidentifyimportantparameterssuchasbandwidth,delay,reliability,security,andmobility,whichareessentialinordertoallowforeffectiveandrobustoperationofthevariousCPSsystems.

OptimalDeploymentofChargingStationsforElectricVehicles:AFormalApproachAmarjitDatta(TennesseeTechnologicalUniversity),BrianLedbetter(TennesseeTechnologicalUniversity),MohammadAshiqurRahman(TennesseeTechnologicalUniversity)

Electricvehicles(EVs)areafascinatinginnovationofthemodernautomobileindustry.Duetotheirattractivefeaturesandagrowingworldwideenvironmentalawareness,thenumberofEVpurchasesisgrowingatanincreasingratedaybyday.AsthepriceofEVsisexpectedtodropinthenearfuture,alargenumberofnewEVswillhittheroadconsequently.However,ourcurrentinfrastructureisnotcapableofsupportingthisgrowingnumberofEVs.Weneedmorechargingstations,placedoptimallyacrossanarea,eachequippedwithmultiplechargingoutletstochargetheincomingEVsinareasonableamountoftime.Inthispaper,wepresentaformalframeworktooptimallydeploychargingstationsforEVsinagivenarea.TheframeworkdesignsthisverificationasanoptimizationproblemwherethegoalistooptimallyplacethechargingstationswithasufficientnumberofchargingoutletstoserveallEVsinagivenareawhilesatisfyingthelimitedbudgetandothersystemconstraints.Weevaluatetheproposedframeworkforitsanalysiscapabilityaswellasitsscalabilitybyexecutingexperimentsondifferentsynthetictestcases.

FormalVerificationofControlStrategiesforaCyberPhysicalSystemAmjadGawanmeh(KhalifaUniversityofScienceandTechnology),AliAlwadi(AucklandUniversityofTechnology),SaziaParvin(UniversityofNewSouthWales)

CyberPhysicalSystems(CPS)useemergingcomputing,communication,andcontrolmethodstomonitorandcontrolgeographicallydispersedcriticalsystemcomponentstoallowahighlevelofconfidenceabouttheiroperation.Simulationmethodsarefrequentlyusedintestingsuchcriticalsystemcomponents,however,itmightnotbeadequatetoshowtheabsenceoferrorsgiventhecomplexityofthesystemcomponentsundertest.Failureindetectingerrorsinsafetycriticalsystemscanleadtoacatastrophicsituation.Inthispaperweproposeanapproach,basedonsimulationandformalanalysis,forthereliabilityanalysisofCPS.WeillustratethisapproachonanindustrialcasestudythatdemonstratesseveralchallengingfeaturesinthedesignandimplementationofCPS.Experimentalresultsobtainedshowthattheproposedapproachisefficientlyusedinordertotestandverifythefourtanksprocesssystem,wheresimulationresultsshowthevalidityofapproximationandabstractionofthesystem,andformalanalysisisusedtovalidatethatseveraldesignrequirementsweresatisfiedinthecontrolstrategiesproposed.

LightweightDetectionandIsolationofBlackHoleAttacksinConnectedVehiclesSamiAlbouq(OaklandUniversity),ErikFredericks(OaklandUniversity)

ConnectedVehicles(CVs)canbeexposedtoblackholeattacksthatdeceivelegitimatenodesbyfalsifyinganattractiveroutetoadestinationnode.Thisoccurswhenanattackersendsapackettothesourcenodeconfirmingtheexistenceofafreshroute.Inthispaper,weproposeaBlackHoleDetectionProtocol(BlackDP)thatworksonahighwaydividedintoclustersandmonitoredbyRoadSideUnits(RSUs)todetectbothsingleandcooperativeblackholeattacks.EveryRSUistaskedwithperformingbothdetectionandisolationofblackholeattacksfortheirrespectivehighwaysectionafterauthenticationviolationsandsuspiciousrouteestablishmentactivitiesthathavebeenreportedbyalegitimatenode.ThedesigngoalofBlackDPistodecouplethedetectionprocessfrommobilenodesandconstructatrustedsemi-centricdetectionprocessthatcancollectneededinformationforlightweightdetectionandreliableisolationofmaliciousnodes.WevalidateBlackDPinasimulatedhighwayenvironmenttodemonstrateitseffectiveness.

AnewthreatassessmentmethodforintegratinganIoTinfrastructureinaninformationsystemBrunoDorsemaine(OrangeLabs),Jean-PhilippeGaulier(OrangeLabs),Jean-PhilippeWary(OrangeLabs),NizarKheir(Thales),PascalUrien(TelecomParisTech)

Inthispaper,weproposeanewapproachtomanagethethreatsbroughtbyanIoTinfrastructuretoaninformationsystem(IS).WefirstgiveastateofartforinformationsecuritypropertiesinIoTandISbasedonstandardssuchasISO16982andISO27005andapreviouslypublishedtaxonomy.Thenwedetailaninnovativemethod,basedontheevaluationofthreatsbroughtbyanIoTinfrastructureontoanIS.ItisrepresentedasaqualitativematrixbetweenIoTinfrastructurethreatsandtheSecuritypropertiesoftheIS.Themethodisthenappliedtotheusecaseofconnectedlightbulbs.Thankstothisapproach,itispossibletologicallyorganizethreatmanagementwhileintegratinganIoTinfrastructureintoanIS.

ASecurityFrameworkforSDN-enabledSmartPowerGridsUttamGhosh(TennesseeStateUniversity),PushpitaChatterjee(SRMRESEARCHINSTITUTE),SachinShetty(OldDominionUniversity)

Emergingsoftwaredefinednetworking(SDN)paradigmprovidesflexibilityincontrolling,managing,anddynamicallyreconfiguringsmartgridnetworks.ItcanbeseenintheliteraturethatconsiderablylessattentionhasbeengiventoprovidesecurityinSDN-enabledsmartgridnetworks.Mostoftheeffortsfocusonprotecting

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smartgridnetworksagainstvariousformsofoutsiderattacksonlybyprovidingconsistentaccesscontrol,applyingefficientandeffectivesecuritypolicies,andmanagingandcontrollingthenetworkthroughtheuseofacentralizedSDNcontroller.Furthermore,centralizedSDNcontrollersareplaguedbyreliabilityandsecurityissues.ThispaperpresentsaframeworkwithmultipleSDNcontrollersandsecuritycontrollersthatprovidesasecureandrobustsmartgridarchitecture.TheproposedframeworkdeploysalocalIDSinasubstationtocollectthemeasurementdataperiodicallyandtomonitorthecontrol-commandsthatareexecutedonSCADAslaves.AglobalIDSincontrolcentercollectsthemeasurementdatafromthesubstationsandestimatesthestateofthesmartgridsystembyutilizingthetheoryofdifferentialevolution.TheglobalIDSfurtherverifiestheconsequencesofcontrol-commandsissuedbySDNcontrollerandSCADAmaster.Analarmisgeneratedupondetectionofanattackerorunsteadystateofthesmartgridsystem.Theframeworkalsodeployslight-weightidentitybasedcryptographytoprotectthesmartgridnetworkfromoutsideattacks.PerformancecomparisonandinitialsimulationresulthavebeenpresentedtoshowthattheproposedframeworkiseffectiveascomparedtoexistingsecurityframeworksforSDN-enabledsmartgrids.

Real-timeMonitoringSteamGeneratorsusingaHybridImagingSystemMahmoudMeribout(PetroleumInstitute),ImranSaied(PetroleumInstitute),EsraAlHosani(AdcoGroup)

Thispaperpresentsahybriddeviceforreal-timemeasurementandimagingofsolidandliquidcontaminantsthatmayoccurinsteamgenerators.ThedeviceusesadedicatedNearInfra-Reddevicetodeterminethetypeofcontaminants(i.e.waterdropletsandironoxideparticles)andaTHzimagingsystemwhichmeasurestheamountofcontaminantsaswellasitsflowrate.TheNIRdevicecanalsodeterminetheconcentrationofcontaminantsatsub-mgaccuracywhenitsvalueisrelativelylowusingspectrometrytechniquecombinedwithprincipalcomponentanalysis(PCA).Threeprincipalcomponents(PC1,PC2,andPC3)wereenoughforthispurpose.ThePCAclassificationwasperformedusingtheleastsquaresupportvectormachine(LS-SVM)method.Incaseofrelativelyhighconcentration,theTHzimagingsystemwhichusesblock-basedmotionestimationalgorithmcandeterminethevelocityofindividualcontaminantparticlestocomputetheglobalmotionvector,theintensityanddirectionofwhichrepresentstheoverallflowrateandflowregimeofthecontaminants.TheusageofimageprocessingtechniquestogetherwithNIRspectrometryconstitutesanewpromisingstepinflowmetering.ThisisdemonstratedbytheextensiveexperimentswhichhavebeenconductedfordifferentscenariowheretheNIRsubsystemsystemcoulddeterminetheconcentrationofwaterdropletsandsolidcontaminantswithamaximumuncertaintyof+/-1.45%and+/-1.16%respectively.WiththeNIRsubsystem,pixel-levelaccuracyofmotionvectorwasachieved,whiletheconcentrationofsolidcontaminantsshowedconsistedproportionalitywiththeaveragepixelintensity.

SecuringbigDataEfficientlythroughMicroaggregationTechniqueandHuffmanCompressionShakilaMahjabinTonni(BangladeshArmyInternationalUniversityofScienceandTechnology),MohammadZahidurRahman(JahangirnagarUniversity),SaziaParvin(UniversityofNewSouthWales),AmjadGawanmeh(KhalifaUniversityofScienceandTechnology)

Cyber-PhysicalSystems(CPS)requiresbigdatacommunicationsaswellasintegrationfromseveraldistributedsources.Thisdatacanusuallybeinterconnectedwithphysicalapplications,suchaspowergridsorSCADAsystems.Inaddition,itcanbepubliclyaccessibleforusingbythirdpartyusersordatascientists.Therefore,itbecomesimperativetoensurethatthisbigdataiswellsecured.Microaggregationisanwidelyusedtechniquetoprotectadatasetthroughanonymityinordertopreventexposureofaperson'sidentity.Thisdatadisclosuremayalsoresultfromanunpredicteddatalinkagewithanotherdataset.As,mostofthesesurveydatasetsstorerecordsusingnumericalvalues,manyofthemicroaggregationtechniquesaredevelopedandtestedonnumericaldata.Thesealgorithmsarenotsuitableforthosedatawherebothnumericalandcategoricaldataarestored.Inthispaperwe'reproposingamicroaggregationtechniqueinordertoprovidedataanonymityregardlessofitstype.TherecordsareclusteredintoseveralgroupsusinganevolutionaryattributegroupingalgorithmandeachgrouprecordsarethenmicroaggregatedapplyingHuffmandatacompressionalgorithm.

ModelBasedEnergyConsumptionAnalysisofWirelessCyberPhysicalSystemsJingLiu(PekingUniversity),PingWang(PekingUniversity),JinlongLin(PekingUniversity),Chao-HsienChu(PennsylvanniaStateUniversity)

Wirelessmeshnetworksbegintobeusedasaninfrastructureofcyber-physicalsystems.Acriticalissueindevelopingwirelesscyberphysicalsystems(WCPSs)isthelimitedamountofenergyavailableinthenodes.Energyconsumptionanalysiscanhelpdesignertoconductapower-awaredesignprocess.Inthispaper,weproposeamodelbasedenergyconsumptionanalysisframeworkatarchitecturelevelforWCPSs.Weextracteventchainsfromthearchitecturemodel,withtheenergyconsumptionmodelforprocessingeachtypeofevent,wecanestimatetheenergyconsumptionforeachcontrolloopandeachnode,aswellastheoverallenergyconsumption.AlltheseenergyconsumptionindexescanhelpustodesignaperformanceandenergyconsumptionbalancedWCPS.

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HotPOST 2017 Workshop Abstracts

Router-basedBrokeringforSurrogateDiscoveryinEdgeComputingJulienGedeon(TechnischeUniversitätDarmstadt),ChristianMeurisch(TechnischeUniversitätDarmstadt),DishaBhat(TechnischeUniversitätDarmstadt),MichaelStein(TechnischeUniversitätDarmstadt),LinWang(TechnischeUniversitätDarmstadt),MaxMühlhäuser(TechnischeUniversitätDarmstadt)

In-networkprocessingpushescomputationalcapabilitiesclosertotheedgeofthenetwork,enablingnewkindsoflocation-aware,real-timeapplications,whilepreservingbandwidthinthecorenetwork.Thisisdonebyoffloadingcomputationstomorepowerfulorenergy-efficientsurrogatesthatareopportunisticallyavailableatthenetworkedge.Inmobileandheterogeneoususagecontexts,thequestionariseshowaclientcandiscoverthemostappropriatesurrogateinthenetworkforoffloadingatask.Inthispaper,weproposeabrokeringmechanismthatmatchesaclientwiththebestavailablesurrogate,basedonspecifiedrequirementsandcapabilities.Thebrokerisimplementedonstandardhomerouters,andthus,leveragestheubiquityofsuchdevicesinurbanenvironments.Tomotivatethefeasibilityofthisapproach,weconductacoverageanalysisbasedoncollectedaccesspointlocationsinamajorcity.Furthermore,thebrokeringfunctionalityintroducesonlyaminimalresourceoverheadontheroutersandcansignificantlyreducethelatencycomparedtodistant,cloud-basedsolutions.

ModelingtheSpreadofInfluenceforIndependentCascadeDiffusionProcessinSocialNetworksZeshengChen(IndianaUniversity-PurdueUniversityFortWayne),KurtisTaylor(IndianaUniversity-PurdueUniversityFortWayne)

Modelingthespreadofinfluenceinonlinesocialnetworksisimportantforpredictingtheinfluenceofindividualsandbetterunderstandingmanyscenariosinsocialnetworks,suchastheinfluencemaximizationproblem.Thepreviousworkonmodelingthespreadofinfluencemakestheassumptionthatthestatusesofnodesinanetworkareindependentofeachother,whichisapparentlynotcorrectforsocialnetworks.Thegoalofthisworkistoderiveanaccuratemathematicalmodeltocharacterizethespreadofinfluencefortheindependentcascadediffusionprocessinonlinesocialnetworks.Specifically,weapplythesusceptible-infected-recoveredepidemicmodelfromepidemiologytocharacterizetheindependentcascadediffusionprocessandderiveageneralmathematicalframework.Toapproximatethecomplexspatialdependenceamongnodesinanetwork,weproposeaMarkovmodeltopredictthespreadofinfluence.Throughtheextensivesimulationstudyoverseveralgeneratedtopologiesandarealcoauthorshipnetwork,weshowthatourdesignedMarkovmodelhasmuchbetterperformancethantheexistingindependentmodelinpredictingtheinfluenceofindividualsinonlinesocialnetworks.

ThankYouForBeingAFriend:AnAttackerViewonOnline-Social-Network-basedSybilDefensesDavidKoll(UniversityofGoettingen),MartinSchwarzmaier(UniversityofGoettingen),JunLi(UniversityofOregon),Xiang-YangLi(UniversityofScienceandTechnologyofChina),XiaomingFu(UniversityofGoettingen)

OnlineSocialNetworks(OSNs)havebecomearewardingtargetforattackers.OneparticularlypopularattackistheSybilattack,inwhichtheadversarycreatesmanyfakeaccountscalledSybilsinorderto,forinstance,distributespamormanipulatevotingresults.AfirstgenerationofdefensesystemstriedtodetecttheseSybilsbyanalyzingchangesinthestructureoftheOSNgraph---unfortunatelywithlimitedsuccess.Basedontheseeffortsasecondgenerationofsolutionsenrichesthegraph-structuralapproacheswithhigher-leveluserfeaturesinordertodetectSybilnodesmoreefficiently.Inthisworkweprovideanin-depthanalysisofthesedefenses.Wedescribetheircommondesignandworkingprinciples,analyzetheirvulnerabilities,anddesignsimpleyeteffectiveattackstrategiesthatanadversarycouldlaunchtocircumventthesesystems.InourevaluationwerevealthatanmiscreantcanexploitthecredulityofOSNusersandfollowatargetedattackstrategytosuccessfullyavoiddetectionbyallexistingapproaches.

EfficientDynamicServiceFunctionChainCombinationofNetworkFunctionVirtualizationWenkeYan(BeijingUniversityofPostsandTelecommunications),KonglinZhu(BeijingUniversityofPostsandTelecommunications),LinZhang(BeijingUniversityofPostsandTelecommunications),SixiSu(BeijingUniversityofPostsandTelecommunications)

NetworkFunctionVirtualization(NFV)andSoftwareDefinedNetwork(SDN)arerecentlyintroducedtoprovidethevirtualizationtechnologyfortacklingthedeploymentofnetworkservicefunctionsincorporatenetworks,broadbandaccessnetworks,andmorerecentlyindatacenters.Howtoenhancetheflexibility,efficiencyandeffectiveofservicefunctiondeploymentisfullofchallenge.AlthoughServiceFunctionChain(SFC)iscarriedouttosupporttheflexibilityofnetworkservices,itstillneedsonestepforwardtofulfilltheefficientandeffectivecombinationofnetworkservices.Inthispaper,weproposeanorthogonalcrossoverdifferentialevolution(OXDE)tooptimizeSFCcombinationwithrespecttoprocessingdelay,energyconsumption,andpacketlossrate.TheevaluationresultsshowthattheproposedOXDEalgorithmoutperformstheotheralgorithmsanditcanachievetheefficiencyandeffectivenessofSFCcombination.

WhenAugmentedRealitymeetsBigDataCarlosBermejo(TheHongKongUniversityofScienceandTechnology),ZhanpengHuang(TheHongKongUniversityofScienceandTechnology),TristanBraud(TheHongKongUniversityofScienceandTechnology),PanHui(TheHongKongUniversityofScienceandTechnology)

Weliveinanerawhereweareoverloadedwithdata,andthiscanbethekeyforgainingrichinsightsaboutourworld.Augmentedreality(AR)enablesusthepossibilitytovisualiseandanalysethegrowingtorrentofdatainainteractive,usablecanvas.Wecandisplaycomplexdatastructuresinsimplerandmoreunderstandablewaysthatwasnotpossiblebefore.BigDataisanewparadigmresultsfromthemyriaddatasourcessuchastransactions,Internet,socialnetworks,healthcaredevicesandsensornetworks.ARandbigdatahavealogicalmaturitythatinevitablywillconverge.ThetreadofharnessingARandbigdatatobreednewinterestingapplicationsisstartingtohaveatangiblepresence.Inthispaper,weexplorethepotentialtocapturevaluefromthemarriagebetweenARandbigdatatechnologies,followingwithseveralchallengesthatmustbeaddressedtofullyrealizethispotential.

SamplingBasedEfficientAlgorithmtoEstimatetheSpectralRadiusofLargeGraphsSamarAbbas(LahoreUniversityofManagementSciences),JuvariaTariq(LahoreUniversityofManagementSciences),ArifZaman(LahoreUniversityofManagementSciences),ImdadullahKhan(LahoreUniversityofManagementSciences)

Evaluatinganextremelyusefulgraphproperty,thespectralradius(largestabsoluteeigenvalueofthegraphadjacencymatrix),forlargegraphsrequiresexcessivecomputingresources.Thisproblembecomesespeciallychallenging,forinstancewithdistributedorremotestorage,whenaccessingthewholegraphitselfisexpensiveintermsofmemoryorbandwidth.Oneapproachtotacklethischallengeistoestimatethespectralradiusofthegraphwhilereadingonlyasmallportion

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ofthegraph.Inthispaperwepresentasamplingapproachtoestimatethespectralradiusoflargegraphs.Wedefineascoreforverticesthati)ismoreofacombinatorialnatureandiseasiertocomputeandii)hassolidtheoreticaljustificationshence,itcloselyapproximateavertex'scontributiontothelargesteigenvalueofthegraph.Usingthisscore,wemodelthesamplingproblemasabudgetedoptimizationproblemanddesignagreedyalgorithmtoselectasubgraphwhosespectralradiusapproachesthatofthewholegraph.Weprovideanalyticalboundoncomputationalcomplexityofouralgorithm.Wedemonstrateeffectivenessofouralgorithmonvarioussyntheticandreal-worldgraphsandshowthatouralgorithmalsoempiricallyoutperformsknowntechniques.Furthermore,wecomparethequalityofourresultstoestimatesobtainedfromwellknownupperandlowerboundsknowninthespectralgraphtheoryliterature.

ExtemporaneousMicro-MobileServiceExecutionWithoutCodeSharingZhengSong(VirginiaTech),MinhLe(UtahStateUniversity),Young-WooKwon(UtahStateUniversity),EliTilevich(VirginiaTech)

Inmobileedgecomputing,amobileorIoTdevicerequestsanearbydevicetoexecutesomefunctionalityandreturnbacktheresults.However,theexecutablecodemusteitherbepre-installedonthenearbydeviceorbetransferredfromtherequesterdevice,reducingtheutilityorsafetyofdevice-to-devicecomputing,respectively.Toaddressthisproblem,wepresentamicro-servicemiddlewarethatexecutesservicesonnearbymobiledevices,withatrustedmiddlemandistributingexecutablecode.Oursolutioncomprises(1)atrustedstoreofvettedmobileservices,self-containedexecutablemodules,downloadedtodevicesandinvokedatruntime;and(2)amiddlewaresystemthatmatchesservicerequirementstoavailabledevicestoorchestratethedevice-to-devicecommunication.Ourexperimentsshowthatoursolution(1)enablesexecutingmobileservicesonnearbydevices,withoutrequiringadevicetoreceiveexecutablecodefromanuntrustedparty;(2)supportsmobileedgecomputinginpracticalsettings,increasingperformanceanddecreasingenergyconsumption;(3)reducesthemobiledevelopmentworkloadbyreusingservices.

PreventingColludingIdentityCloneAttacksinOnlineSocialNetworksGeorgesA.Kamhoua(FloridaInternationalUniversity),NikiPissinou(FloridaInternationalUniversity),S.S.Iyengar(FloridaInternationalUniversity),JonathanBeltran(FloridaInternationalUniversity),CharlesKamhoua(AirForceResearchLaboratory),BrandonLHernandez(UTRGV),LaurentNjilla(AirForceResearchLaboratory)

Nowadays,OnlineSocialNetworks(OSNs)becomeoneofthemostcommonwayamongstpeopletofacilitatecommunication,thishasmadeitatargetforattackerstostealinformationfrominfluentialusers.ThishasbroughtnewformsofcustomizedattacksforOSNs.AttackerstakeadvantageoftheusertrustworthinesswhenusingOSN.Thisexploitationleadstoattackswithacombinationofbothclassicalandmodernthreats.Specifically,colludingattackershavebeentakenadvantageofmanyOSNsbycreatingfakeprofilesoffriendsofthetargetinthesameOSNorothers.Colludersimpersonatetheirvictimsandaskfriendrequeststothetargetintheaimtoinfiltrateherprivatecircletostealinformation.ThistypeofattacksaredifficulttodetectinOSNsbecausemultiplemalicioususersmayhaveasimilarpurposetogaininformationfromtheirtargeteduser.Inthispaper,toovercomethistypeofattack,wefirstaddresstheproblemofmatchinguserprofilesacrossmultipleOSNs,second,byusingbothtextualandfeaturesextractedfromuserprofileandbasedonsupervisedlearningtechniques,webuildaclassifier.Simulationandexperimentalresultsareprovidedtovalidatetheaccuracyofourfindings.

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IoTCA 2017 Workshop Abstracts

TowardsPrivacy-AwareSmartBuildings:Capturing,Communicating,andEnforcingPrivacyPoliciesandPreferencesPrimalPappachan(UniversityofCaliforniaIrvine),MartinDegelingy(CarnegieMellonUniversity),RobertoYus(UniversityofCaliforniaIrvine),AnupamDasy(CarnegieMellonUniversity),SrutiBhagavatulay(CarnegieMellonUniversity),WilliamMelichery(CarnegieMellonUniversity),PardisEmamiNaeiniy(CarnegieMellonUniversity),ShikunZhangy(CarnegieMellonUniversity),LujoBauery(CarnegieMellonUniversity),AlfredKobsa(UniversityofCaliforniaIrvine),SharadMehrotra(UniversityofCaliforniaIrvine),NormanSadeh(CarnegieMellonUniversity),NaliniVenkatasubramanian(UniversityofCaliforniaIrvine)

TheInternetofThings(IoT)ischangingthewayweinteractwithoursurroundingenvironmentindomainsasdiverseashealth,transportation,officebuildingsorourhomes.Insmartbuildingenvironments,informationcapturedaboutabuilding’sinfrastructureanditsinhabitantswillhelpdevelopservicesthatcanhelpusbecomemoreproductive,increaseourcomfort,enhanceoursocialinteractions,increasesafety,saveenergyandmore.Butbyrelyingonthecollectionandsharingofinformationaboutabuilding’sinhabitantsandtheiractivities,theseservicesalsoopenthedoortoprivacyrisks.Inthispaper,weintroduceaframeworkwhereIoTAssistantscaptureandmanagetheprivacypreferencesoftheirusersandcommunicatethemtoprivacy-awaresmartbuildings,whichenforcethemwhencollectinguserdataorsharingitwithbuildingservices.Weoutlineelementsofaninfrastructurenecessarytosupportsuchinteractionsandalsodiscussimportantprivacypolicyattributesthatneedtobecaptured.Thisincludeslookingatattributesnecessarytodescribe–(1)thedatacollectionandsharingpracticesassociatedwithdeployedsensorsandservicesinsmartbuildingsaswellas(2)theprivacypreferencesweneedtocapturetohelpusersmanagetheirprivacyinsuchenvironments.

DeployingData-DrivenSecuritySolutionsonResource-ConstrainedWearableIoTSystemHangCai(WorcesterPolytechnicInstitute),TianlongYun(WorcesterPolytechnicInstitute),JosiahHester(DartmouthCollege),KrishnaK.Venkatasubramanian(ClemsonUniversity)

WearableInternet-of-Things(WIoT)environmentshavedemonstratedgreatpotentialinabroadrangeofapplicationsinhealthcareandwell-being.SecurityisessentialforWIoTenvironments.LackofsecurityinWIoTsnotonlyharmsuserprivacy,butmayalsoharmtheuser’ssafety.ThoughdevicesintheWIoTcanbeattackedinmanyways,inthispaperwefocusonadversarieswhomountwhatwecallsensorhijackingattacks,whichpreventtheconstituentmedicaldevicesfromaccuratelycollectingandreportingtheuser’shealthstate(e.g.,reportingoldorwrongphysiologicalmeasurements).Inthispaperweoutlinesomeofourexperiencesinimplementingadatadrivensecuritysolutionfordetectingsensor-hijackingattackonasecurewearableinternet-of-things(WIoT)basestationcalledtheAmulet.Giventhelimitedcapabilities(computation,memory,batterypower)oftheAmuletplatform,implementingsuchasecuritysolutionisquitechallengingandpresentsseveraltradeoffswithrespecttoresourcesrequirements.WeconcludethepaperwithalistofinsightsintowhatcapabilitiesconstrainedWIoTplatformsshouldprovidedeveloperssoastomaketheinclusionofdata-drivensecurityprimitivesonsuchsystemseasy.

AMotifbasedIoTFrameworkforDataEfficiencyAkashSahoo(TexasA&MUniversity),RabiMahapatra(TexasA&MUniversity)

InternetofThings(IoT)hasallowedembeddeddevicestoconnecttothevastInternetnetworkworldwide.WithbillionsofIoTdeviceswaitingtobeconnected,itisnecessarytobuildefficientinfrastructuretohandlelargeamountofdataforefficientstorageandnetworktraffic.TheamountofdatacreatedattheIoTedgesisregardedasonethebiggestchallengesofIoT.Thispaperproposesamotif-basedencodingschemeforIoTframeworkthathelpstoreducedatageneratedbysensorsatedgenodes.Thissimpleencodingfeatureresidesinboththeserverandtheenddeviceslikeinserver-clientmodel.Ourexperimentsdemonstratedtheschemesbenefitsbyusingslowandfastbaudratesensorssuchastemperatureandaccelerometerrespectivelyasthecasestudies.Theresultsobtainedshowtheproposedmotifbasedframeworkreducesthedataredundancyuptotwoordersofmagnitudewhileretainingmorethan80%accuracytowardsmotifrecognition.

CoTWare:ACloudofThingsMiddlewareJameelaAl-Jaroodi(RobertMorrisUniversity),NaderMohamed(MiddlewareTechnologiesLab.),ImadJawhar(MidcompResearchCenter)

Therearemanyapplicationsthatrequireintegratingalargenumberofphysicalobjectsanddevicesinalarge-scaleInternetofThings(IoT)networks.Someexamplesoftheseapplicationsaresmartgrids,smartwaternetworks,andintelligenttransportationsystems.Theseapplicationsneedreal-timecontrols,powerfulandscalabledatastorageandprocessingcapabilities,andadvanceddataanalyticsmechanisms.OneofthepromisingtechnologiestosupportsuchapplicationsistheCloudofThings(CoT).CoTcanprovideaplatformforlinkinganIoTwithCloudComputing(CC).AnothertechnologythatcanbeutilizedforenhancingIoTapplicationsisFogComputing,whichextendsthetraditionalCloudComputingparadigmtotheedgeofthenetworktoenablebettersupportforoperatingenhancedservices.However,properintegrationandefficientutilizationofCoTandFogComputingforlarge-scaleIoTapplicationsisnotaneasytask.Thispaperproposesaservice-orientedmiddleware,calledCoTWare,tofacilitateeffectiveintegrationandutilizationofCoTandFogComputingforlarge-scaleIoTapplications.

SecuringtheInternetofThings:AMeta-StudyofChallenges,Approaches,andOpenProblemsMahmudHossain(UniversityofAlabamaatBirmingham),RagibHasan(UniversityofAlabamaatBirmingham),AnthonySkjellum(AuburnUniversity)

TheInternetofThings(IoT)isbecomingakeyinfrastructureforthedevelopmentofsmartecosystems.However,theincreaseddeploymentofIoTdeviceswithpoorsecurityhasalreadyrenderedthemincreasinglyvulnerabletocyberattacks.Insomecases,theycanbeusedasatoolforcommittingseriouscrimes.AlthoughsomeresearchershavealreadyexploredsuchissuesintheIoTdomainandprovidedsolutionsforthem,thereremainstheneedforathoroughanalysisofthechallenges,solutions,andopenproblemsinthisdomain.Inthispaper,weconsiderthisresearchgapandprovideasystematicanalysisofsecurityissuesofIoT-basedsystems.Then,wediscusscertainexistingresearchprojectstoresolvethesecurityissues.Finally,wehighlightasetofopenproblemsandprovideadetaileddescriptionforeach.WepositthatoursystematicapproachforunderstandingthenatureandchallengesinIoTsecuritywillmotivateresearcherstoaddressingandsolvingtheseproblems.IndexTerms—InternetofThings;SecurityIssue;AttackSur-face;AttackTaxonomy;IoTForensics.

InternetofThingsFrameworkforSmartLearningAnalyticsAliYavari(SwinburneUniversityofTechnology),RezaSoltanpoor(RMITUniversity)

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LearningAnalytics(LA)hasbecomeaprominentparadigminthecontextofeducationlatelywhichadoptstherecentadvancementsoftechnologysuchascloudcomputing,bigdataprocessing,andInternetofThings.LAalsorequiresanintensiveamountofprocessingresourcetogeneraterelevantanalyticalresults.However,thetraditionalapproacheshavebeeninefficienttotackleLAchallengessuchasreal-time,highper-formance,andscalableprocessingofheterogeneousdatasetsandstreamingdata.AnInternetofThings(IoT)scalable,distributedandhighperformanceframeworkhasthepotentialtoaddressmentionedLAchallengesbyefficientcontextualizationofdata.Inthispaper,SmartLearningAnalyticsconceptualmodelisproposedtoimprovetheeffectivenessofLAbyutilizinganIoT-basedplatformintermsofperformance,scalability,andefficiency.

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JCC 2017 Workshop Abstracts

HeterogeneousMalwareSpreadProcessinStarNetworkLiboJiao(TsinghuaUniversity),HaoYin(TsinghuaUniversity),DongchaoGuo(TsinghuaUniversity),YongqiangLyu(TsinghuaUniversity)

TheheterogeneousSISmodelforvirusspreadinanyfinitesizegraphcharacterizestheinfluenceoffactorsofSISmodelandcouldbeanalyzedbytheextendedN-Intertwinedmodelintroducedin[1].Wespecificallyfocusontheheterogeneousvirusspreadinthestarnetworkinthispaper.Theepidemicthresholdandtheaveragemeta-stablestatefractionofinfectednodesarederivedforvirusspreadinthestarnetwork.OurresultsillustratetheeffectofthefactorsofSISmodelonthesteadystateinfection.

CostReductioninHybridCloudsforEnterpriseComputingBiyuZhou(InstituteofComputingTechnology,ChineseAcademyofSciences),FaZhang(InstituteofComputingTechnology,ChineseAcademyofSciences),JieWu(TempleUniversity),ZhiyongLiu(InstituteofComputingTechnology,ChineseAcademyofSciences)

Hybridcloud-baseddeploymentisatrendincloudcomputingwhichenablesenterprisetobenefitfromcloudinfrastructureswhilehonoringprivacyrestrictionsonsomeservices.Enterpriseapplicationmigrationisaneffectivewaytoimprovetheefficiencyofusingthecloudinfrastructures.However,itisachallengingproblemtodecidewhichpartsoftheapplicationstomigrateandwheretomigrate.Inthispaper,wefocusontheproblemofplanningthemigrationofenterpriseapplicationsinhybridcloudinfrastructures.Unlikepreviousstudies,weconsiderageneralhybridcloudarchitecturethatinvolvesmultiplepubliccloudsratherthanonlyone.Ouraimistomaximizetheenterprisecostreductionundertheconstraintofuserexperienceintermsofresponsetime.Wefirstformulatethepplicationmigrationproblemasanoptimizationproblem.AwareofitsNP-hardness,wedesignanefficientmigrationframeworktoapproximatetheoptimumforalargeproblemsize.First,weleveragetheapplicationcharacteristictoreducethescaleoftheproblembydividingitintomultiplesmallersubproblems.Then,anefficientalgorithmbasedondynamicprogrammingisproposedtosolvethesmallscalesubproblems.Finally,weconstructafeasiblesolutiontotheoriginalproblem.Simulationresultsdemonstratethatourframeworkcanbringsignificantbenefitstoenterprises.

DC-RSF:ADynamicandCustomizedReputationSystemFrameworkforJointCloudComputingFanghuaYe(SunYat-senUniversity),ZibinZheng(SunYat-senUniversity),ChuanChen(SunYat-senUniversity),YurenZhou(SunYat-senUniversity)

Jointcloudcomputing(JointCloud),asabrand-newparadigmofcloudcomputing,aimsatbuildingacloudecosystem,inwhichendusersareagnostictocloudservicevendorsasapplicationsandservicesarebuiltuponvirtualclouds.IncaseoflowqualitycloudresourcesprovideddeliberatelyandinordertofacilitatethepersistentandsounddevelopmentofJointCloudecosystem,weproposeadynamicandcustomizedreputationsystemframework(DC-RSF)toevaluatethecredibilityofcloudservicevendors.AtthecoreofDC-RSFisthecustomizedanddynamiccredibilitymodel(CDCM),whichcalculatescreditvalueforeachcloudservicevendorbasedonservicerequirementsofendusersandcredentialattributesofcloudservicevendors.WefurtherincorporateaBlockchain-basedmoduleintoDC-RSFtopreventthecreditvaluefrombeingartificiallytampered.

WebServiceApplianceBasedonUnikernelKaiYu(NationalLabforParallelandDistributedProcessing),ChengfeiZhang(NationalLabforParallelandDistributedProcessing),YunxiangZhao(NationalLabforParallelandDistributedProcessing)

Mini-OSisatinyOS(operatingsystem)kerneldistributedwithXenProjectHypervisor.ItismainlyusedasanOSforstubdomainaimedatDom0disaggregationandalsoasteppingstoneforUnikerneldevelopment.WeimplementedasimplehttpserveronMini-OS,andbuiltMini-OSintoawebserviceappliance.WeevaluateditsperformancecomparedwiththesameimplementedserveronUbuntuPV(para-virtualization)DomU,andachievedabout39%performanceimprovement.TheresultsshowsthatMini-OScanbeawebserviceapplianceandhasagoodperformance.

AnalysisandEvaluationoftheGASModelforDistributedGraphComputationWangJinyan(NationalLabforParallelandDistributedProcessing),ZhangChengfei(NationalLabforParallelandDistributedProcessing)

Comparedwithdistributedgraphcomputation,traditionallysinglenodecomputationisunfittedinprocessinglargescalegraphdata.TheGAS(Gather,ApplyandScatter)Modelisauniversalvertex-cutgraphcomputationprogrammingmodelbasedonedge-centricprogramstosupportgraphalgorithms,whichprocessdistributedgraphcomputationaftergraphpartition.Inthispaper,weintroducethatthreeminor-stepsofGAS.WethenanalyzemorecompleteprocessofGASconsideringintra-nodecomputationandinter-nodecommunicationofdistributedgraphcomputation.Basedonouranalysis,weevaluatetheperformanceindifferentnodesofgraphanalysisalgorithmapplyingGASmodel.Theevaluationshowsthatthebottleneckiscomputationperformanceorcommunicationbandwidthdependingonnumberofnodes,whichisaninspirationofoptimizingtheGASmodel.

TrafficSignsDetectionBasedonFasterR-CNNZhongrongZuo(NationalLabforParallelandDistributedProcessing),KaiYu(NationalLabforParallelandDistributedProcessing),QiaoZhou(NationalLabforParallelandDistributedProcessing),XuWang(NationalLabforParallelandDistributedProcessing),TingLi(NationalLabforParallelandDistributedProcessing)

Inthispaper,weuseaadvancedmethodcalledFasterR-CNNtodetecttrafficsigns.Thisnewmethodrepresentsthehighestlevelinobjectrecognition,whichdon'tneedtoextractimagefeaturemanuallyanymoreandcansegmentimagetogetcandidateregionproposalsautomatically.Ourexperimentisbasedonatrafficsigndetectioncompetitionin2016byCCFandUISEEcompany.ThemAPvalueoftheresultis0.3449thatmeansFasterR-CNNcanindeedbeappliedinthisfield.Eventhoughtheexperimentdidnotachievethebestresults,weexploreanewmethodintheareaofthetrafficsignsdetection.Webelievethatwecangetabetterachievementinthefuture.

JCLedger:ABlockchainBasedDistributedLedgerforJointCloudComputing

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XiangFu(NationalUniversityofDefenseTechnology),HuaiminWang(NationalUniversityofDefenseTechnology),PeichangShi(NationalUniversityofDefenseTechnology),YingweiFu(NationalUniversityofDefenseTechnology),YijieWang(NationalUniversityofDefenseTechnology)

WiththedevelopmentofEconomicGlobalization,traditionalsingle-cloudproviderscannotmeettheneedsoftheexplosive,global,diversecloudservices.JointCloudaimsatempoweringthecooperationamongmultipleCloudServiceProviders(CSP)toprovidecross-cloudservices.OurworkinthispaperismainlyfocusedontheaccountingtechnologyforJointCloudcomputingandweproposetheJCLedger-ablockchainbaseddistributedledger.AnewparticipantCCP(CryptocurrencyProvider)isintroducedintotheJointCloudcollaborationenvironmenttoprovidethecryptocurrencytransferred.WehaveadetaileddescriptionofJCLedgermodel.WefurtheranalyzethefourmostimportantmechanismsforJCLedgerandprovidebasicperspectivesforin-depthanalysis.Finally,wediscusstheinnovationsofJCLedgerandourfutureworkinthisfield.

CorporationArchitectureforMultipleCloudServiceProvidersinJointCloudComputingPeichangShi(NationalUniversityofDefenseTechnology),HuaiminWang(NationalUniversityofDefenseTechnology),XikunYue(NationalUniversityofDefenseTechnology),ShilanYang(NationalUniversityofDefenseTechnology),ShangzhiYang(NationalUniversityofDefenseTechnology),YuxingPeng(NationalUniversityofDefenseTechnology)

Nowadays,cloudcomputingishardtoeffectivelysustaintheimplementationofthecommercialmodelofInternetServiceglobalization.Thereisagrowingtrendtobuildanenvironmentofcloudservice,withthecapacitytoserveanytimeandanywhere,bymutualcooperationbetweencloudserviceprovidersaroundtheworld.However,thistendencywillraiseakeyissuewhichishowtoprovideabenignenvironment,thatallowsself-collaborationandfaircompetition,fordifferentcloudserviceproviderswithdiversestakeholder.GuidedbytheconceptandstructureofService-OrientedArchitecture(SOA)service,thispaperproposesastructurenamedJointCloudCorporationEnvironment(JCCE),whichoffersamutualbenefitandwin-winJointCloudenvironmentforglobalcloudserviceproviders.JCCEcontainsthreecoreservices,whichareDistributedCloudTransaction,DistributedCloudCommunityandDistributedCloudSupervision.Also,facingwithdifferentcloudserviceparticipants,JCCEoffersthreemainservicemodesfortheirconsumption,supplyandcoordination.Thisstudyplaysasignificantroleinsupportingthesharingandself-collaborationofmultiplecloudentities,andpromotingthedevelopmentofcloudservicemarkethealthyandorderly.

SharingPrivacyDatainSemi-TrustworthyStoragethroughHierarchicalAccessControlYuzhaoWu(TsinghuaUniversity),YongqiangLyu(TsinghuaUniversity),QianFang(TsinghuaUniversity),GengZheng(TsinghuaUniversity),HaoYin(TsinghuaUniversity),YuanchunShi(TsinghuaUniversity)

Dataoutsourcingincloudisemergingasasuccessfulparadigmthatbenefitsorganizationsandenterpriseswithhigh-performance,low-cost,scalabledatastorageandsharingservices.However,thisparadigmalsobringsforthnewchallengesfordataconfidentialitybecausetheoutsourcedarenotunderthephysiccontrolofthedataowners.Theexistingschemestoachievethesecurityandusabilitygoalusuallyapplyencryptiontothedatabeforeoutsourcingthemtothestorageserviceproviders(SSP),anddisclosethedecryptionkeysonlytoauthorizeduser.Theycannotensurethesecurityofdatawhileoperatingdataincloudwherethethird-partyservicersareusuallysemi-trustworthy,andneedlotsoftimetodealwiththedata.WeconstructaprivacydatamanagementsystemappendinghierarchicalaccesscontrolcalledHAC-DMS,whichcannotonlyassuresecuritybutalsosaveplentyoftimewhenupdatingdataincloud.

AReliabilityBenchmarkforBigDataSystemsonJointCloudYingyingZheng(InstituteofSoftware,ChineseAcademyofSciences),LijieXu(InstituteofSoftware,ChineseAcademyofSciences),WeiWang(InstituteofSoftware,ChineseAcademyofSciences),WeiZhou(KSYUN),YingDing(ChangchunUniversityofScienceandTechnology)

JointCloudprovidesaflexibleandelasticcomputingresourceplatform.BigdatasystemssuchasMapReduceandSparkarewidelydeployedonthisplatformforbigdataprocessing.Theseframeworkshavehighscalability,buttheapplicationsrunningatopthemoftengenerateruntimeerrors,suchasoutofmemoryerrors,IOexceptions,andtasktimeouts.Forusers,theywanttoknowwhetherthedevelopedapplicationshavepotentialapplicationfaults.Forsystemdesignersandmanagers,theywanttoknowwhetherthedeployed/updatedframeworkshavepotentialsystemfaults.Currentperformancebenchmarkingcanchoosesuitablecloudsplatformforcustomers.However,theydonotconsiderreliabilityofapplicationsdeployedonthecloud.Inaddition,currentbenchmarksforbigdatasystemarealsoonlydesignedforperformancetesting.Tofillthisgap,weproposeareliabilitybenchmark,whichcontainsrepresentativeapplications,anabnormaldatagenerator,andaconfigurationcombinationgenerator.Differentfromperformancebenchmarks,thisbenchmark(1)generatesabnormaltestdataaccordingtotheapplicationcharacteristics,and(2)reducestheconfigurationcombinationspacebasedonconfigurationfeatures.Currently,weimplementedthisbenchmarkonSparkframework.Inourpreliminarytest,wefoundthreetypesoferrors(i.e.,outofmemoryerror,timeoutandwrongresults)infiveSQL,MachineLearning,andGraphapplications.

UCPR:UserClassificationandInfluenceAnalysisinSocialNetworkCongZha(TsinghuaUniversity),YongqiangLv(TsinghuaUniversity)

Therearevigorousdevelopmentsofsocialnetworkwhichaffectoutlifegreatly.Userinfluenceisanimportantreasontopro-motetheinteractioninsocialnetwork.Whenweanalyzeuserinfluence,singlevaluecan’tindicatetheuserinfluenceindifferentdomains.ThispaperputsforwardthedesignofUserClassi-ficationPageRank(UCPR)tosolvethisproblem.Firstly,weclassifyusersaccordingtothecontentwhichtheyforwarded.Then,weusespacemappingtosetupseveralsubnet.Finally,weanalyzeuserinfluenceineveryspecificsubnetbyDomainMappedNetwork(DMN)whichisbasedonPageRankalgorithmandweimprovethisalgorithmtoanalyzetheuserinfluenceindifferentdomains.Throughtheworkofthispaper,weusedavectortopresentuserinfluenceratherthanasinglenumberandwetestandverifiedthelong-taileddistributionsofsocialnet-workinexperiments.

AdaptiveRoutingAlgorithmforJointCloudVideoDeliveryZexunJiang(TsinghuaUniversity),HaoYin(TsinghuaUniversity)

AstheInternetkeepsgrowing,onlinevideohasbecomeagreatpartofthecurrentInternetdatatraffic,whichwilltakeover80%ofInternettrafficaccordingtoCiscosreport.Also,newandmoreheavyweightapplicationskeepdevelopingtofulfillpeople’sgrowingrequirements,like4kresolutionandvisualrealityvideos.However,onesingleserviceprovider,likeaContentDeliveryNetwork(CDN),cannotmeettheperformancerequirementscompletely.ToemploythepotentialofJointCloud,thispaperdesignsandimplementsanewrequestroutingalgorithmthatcanmakevideodeliveryutilizemultiplecloudsandservers.Onthepremiseofguaranteeingthequalityofvideoplaying,thisalgorithmminimizesthecostofserviceresourcesbasedondifferentinfrastructuresservicequality,cost,andcover

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areas.Basedonthisalgorithm,weimplementapracticalvideodeliverysystemusinglight-weight,flash-basedterminals.Andthissystemprovideslivevideoandvideo-on-demanddeliveryserviceforChinaFutureNetworksIndustrySummit2014onJune4th.Theactualuserdatawasgatheredandanalyzedtoverifytheeffectivenessofthisalgorithm.90.2%ofthetotalVODrequestswerecompletedsmoothlywithoutpause,andthevideotrafficwasoptimizedbythealgorithm.

TowardsEfficientResourceManagementinVirtualCloudsBoAn(PekingUniversity),JunmingMa(PekingUniversity),DonggangCao(PekingUniversity),GangHuang(PekingUniversity)

Theuseofmultiplecloudsbringsmanyadvantages:costoptimization,Quality-of-Service(QoS)improvements,highavailability,avoidanceofvendorlock-in,disasterrecoveryandsoon.However,currentlythecloudvendorislargelyproprietaryanddifferentcloudvendorshavetheirownheterogeneousinfrastructure,makingitdifficultforuserstoutilizeresourcefrommultiplecloudvendors.Asaresult,usershavetomanagedistributedapplicationsspanningmultiplecloudsandtakeintoconsiderationtheservicesmigrationforreasonslikebestcostefficiency.Inthispaper,weintroducesthenotionofVirtualCloudandfocusontheissuesrelatedtomulti-cloudresourcemanagementinVirtualCloud.VirtualCloudisacustomizedcloudbyaggregatingresourcesandservicesofdifferentcloudsandaimstoprovideenduserswithaspecificcloudworkingenvironment.Itwilleaseusers’burdenofresourceanddistributedapplicationmanagementaswellastheworkloadmigrationacrosscloud.

MonitoringandBillingofALightweightCloudSystemBasedonLinuxContainerYujianZhu(PekingUniversity),JunmingMa(PekingUniversity),BoAn(PekingUniversity),DonggangCao(PekingUniversity)

Nowadays,moreandmoreenterprisesandresearchinstituteschoosetobuildmini-datacentersanddeployprivatecloudenvironmentstomeetgrowingbusinessandresearchneeds.Tomakeuserscanrundifferentapplicationframeworksonthesamedatacenter,Caoetal.proposedanewservicemodelnamedClaaS(ClusterasaService)anddevelopedalightweightprototypesystemnamedDockletwhichisbasedonLXC(LinuxContainer).Dockletfacesaproblemofresourceswasteandabuseduetoourfreepolicy.ThispaperintroducesthemonitoringandbillingmodulesofDockletinordertosolvethisproblem.Monitoringmoduleprovidesusersandadministratorswithaclear,real-timeanddetailedmonitoringinterfacetounderstandthestatuesofrunningapplicationsandtheusageofphysicalresources.Billingmoduleusesthesedatatoreminduserstoreleaseunnecessaryresources.Anexperimentandobservationsshowthatourproposedmonitoringmethodiseffectiveandlightweightandourproposedbillingmodelincreasestheutilizationofphysicalresourcesofamini-datacenter.

Buildingemulationframeworkfornon-volatilememoryGuoliangZhu(NationalUniversityofDefenseTechnology),KaiLu(NationalUniversityofDefenseTechnology),XiaopingWang(NationalUniversityofDefenseTechnology)

Currently,researchersusesimulatorstoexperimenttheirinnovationonemergingnon-volatilememory.Unfortunately,simulationmethodisbothtime-consumingandarehardtodebug.Inthispaper,wepresentanon-volatilememoryemulatorwhichenablessystem-levelresearchonemergingmemory.Ouremulatorusesperformancemonitoringunitsonoff-the-shelfprocessorstoimplementanaccureteperformancemodel.

Seflow:EfficientFlowSchedulingforData-ParallelJobsQiaoZhou(NationalLabforParallelandDistributedProcessing),ZiyangLi(NationalLabforParallelandDistributedProcessing),PingZhong(CentralSouthUniversity),TianTian(NationalLabforParallelandDistributedProcessing),YuxingPeng(NationalLabforParallelandDistributedProcessing)

Data-paralleljobstransfermassiveamountsofdatabetweenaseriesofsuccessivestages.Thecoflowabstractionisproposedtorepresentagroupofparallelflowsbetweentwostagesandefficientlyimprovesstagelevelperformance.However,state-of-the-artcoflowschedulingtechniquesareagnostictothejobs’intercoflowsemanticsandthusaresuboptimalinreducingtheaveragejobcompletiontimes(JCT).Toaddressthisproblem,inthispaperwepresentthe“semanticflow”(seflow)abstractiontoexpressthejob-levelintercoflowsemantics.Aseflowcomprisesnotonlyallthecoflowsofajobbutalsotherelationshipbetweenthecoflows.Wedesignanefficientseflowschedulerwhichutilizestherichseflowsemanticsofjobstoachievebetterperformancethanseflow-agnosticschedulingfordataparalleljobs.

OnlineEncodingforErasure-CodedDistributedStorageSystemsFangliangXu(NationalUniversityofDefenseTechnology),YijieWang(NationalUniversityofDefenseTechnology),XingkongMa(NationalUniversityofDefenseTechnology)

Manylarge-scaledistributedstoragesystemsdeployerasurecodingtoprotectdatafromfrequentserverfailuresforcostreason.Inmostofthesesystems,newlyinserteddataisfirstreplicatedacrossdifferentstoragenodesandthenmigratedtoerasurecoded.Althoughthisofflineencodingmannercanimproveperformanceofdataaccessbeforeerasurecodingforsomesystems,ithelpslittleandwastesmanynetworkresourcesanddiskresourcesformanyothersystems.Inthisstudy,weproposeanonlineencodingmethod,whichencodesdataassoonasitisinsertedintothesystem.Byeliminatingthemigrationprocess,ouronlineencodingcansignificantlyreducenetworktransferanddataread;bycachingtheintermediateparityblocksintomemory,ouronlineencodingalsosignificantlyreducedatawrite.Analysisshowthatouronlineencodingcanreducedatatransferbymorethan25%,reducedatawriteby57%atleastandeliminatealldataread,comparedtotraditionalofflineencoding.

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PED 2017 Workshop Abstracts

WED-SQL:ARelationalFrameworkforDesignandImplementationofProcess-AwareInformationSystemsBrunoPadilha(UniversityofSaoPaulo),AndréLuisSchwerz(FederalUniversityofTechnology),RafaelLiberatoRoberto(FederalUniversityofTechnology)

Despitethesignificantevolutionofthedesignandimplementationofbusinessprocessmodels,atransactionalapproachthatevolvesanincrementalandadaptivestrategyremainsanimportantchallengetobeovercome.TraditionalframeworkssuchasBPEL,ProcessAlgebra,andPetriNetrequireanadditionalsoftwarelayerorsomethirdpartytoolkitstobeabletoenforceadata-statebasedtransactioncontrolanddealwithsemanticexceptions.However,thecomplexityofimplementationbasedonthesetraditionalframeworks,especiallytotreatexceptions,istoohigh.Inthispaper,wepresenttheWED-SQL,adistributedframeworkthatprovidesareliableandefficientwaytodesignandimplementbusinessprocesses.OurmaincontributionistheintegrationofWED-flowconceptsintothePostgresSQLRDBMS.ThisintegrationenablestheWED-SQLtotakefulladvantageoftransactionalpropertiesandalsobenefitfromtheSQLlanguagetospecifytheWED-flowdefinitions.

QueryingWorkflowLogsYanTang(UniversityofCaliforniaatSantaBarbara),JianwenSu(UniversityofCaliforniaatSantaBarbara)

Abusinessprocess(BPorworkflow)isanassemblyoftaskstoaccomplishabusinessgoal.Businessprocessmanagement(BPM)isastudytoprovidesupportforthedesign,configuration/implementation,enactmentandmonitoring,diagnose/analysis,andre-designofworkflow.Businessanalyticsorintelligence(BI)isanecessarysteptowardsre-design/improvement.ThetraditionalmethodologyforBIisthewellknownsequenceofETL,data/processwarehouse,andOLAPtools.Inthispaper,wefocusontheproblemofadhocqueryingprocessenactmentsfordata-centricbusinessprocesses.Wedevelopanalgebraicquerylanguagebasedon“incidents”toallowtheusertoformulateadhocqueriesdirectlyonworkflowlogs.Aformalsemanticsandanpreliminaryqueryevaluationalgorithmareprovided.

Ontheintegrationofevent-basedandtransaction-basedarchitecturesforSupplyChainsZhijieLi(IndianaUniversity–PurdueUniversityIndianapolis),HaoyanWu(IndianaUniversity–PurdueUniversityIndianapolis),BrianKing(IndianaUniversity–PurdueUniversityIndianapolis),ZinaBen-Miled(IndianaUniversity–PurdueUniversityIndianapolis),JohnWassick(TheDowChemicalCompany),JeffreyTazelaar(TheDowChemicalCompany)

Affordableandreliablesupplychainvisibilityisbecomingincreasinglyimportantasthecomplexityofthenetworkunderlyingsupplychainsisbecomingordersofmagnitudeshighercomparedtoadecadeago.Moreoverthisincreaseincomplexityisstartingtoreflectonthecostofgoodsandtheiravailabilitytotheconsumers.Thispaperaddressestwokeyissuesinthedistributionphaseofthesupplychain,namely,affordabilityandpseudoreal-timevisibilityoftruckloadactivities.Theproposedframeworkcreatesadigitalthreadthattracksthepseudoreal-timestatusoftheshipmentmakingthephysicaldistributionprocesscompletelytransparenttothestakeholders.Thearchitectureoftheframeworkisbasedonadynamichybridpeer-to-peernetworkandaprivate/publicblockchaindatamodelthatleveragesemergentsensortechnologies.

CacheDOCS:ADynamicKey-ValueObjectCachingServiceJulienGascon-Samson(UniversityofBritishColumbia),MichaelCoppinger(McGillUniversity),FanJin(McGillUniversity),JörgKienzle(McGillUniversity),BettinaKemme(McGillUniversity)

Cachingplaysanimportantroleinmanydomains,asitcanleadtoimportantperformanceimprovements.Akey-valuebasedcachingsystemtypicallystorestheresultsofpopularqueriesinefficientstoragelocation.Whilecachingenjoyswidespreadusageinthecontextofdynamicwebapplications,mostmainstreamcachingsystemsstorestaticbinaryitems,whichmakesthemimpracticalformanyreal-worldapplicationsthatwouldbenefitfromstoringdynamicitems.Inthispaper,weproposeCacheDOCS,adynamickey-valueobjectcachingservicethatallowsforcachingarbitraryobjects.Aspartofourmodel,CacheDOCSprovidesanAPIthatsupportstheexecutionofoperationsagainstcachedobjects,andallowsforclientstoseamlesslysubscribetokeeptheirlocalcopiesinsyncwithcachedremoteobjects.CacheDOCSsupportsmultipleupdatedisseminationstrategiesinordertooptimizeperformance,andproposesaversioningmechanismtoensureconsistency.WeimplementedafullversionofCacheDOCSandweranseveralperformance-relatedexperimentsunderthreeuse-casescenarios.

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PSBD 2017 Workshop Abstracts

Anovelgame-theoreticmodelforcontent-adaptiveimagesteganographyQiLi(HunanUniversity),XinLiao(HunanUniversity),GuoyongChen(HunanUniversity),LipingDing(GuangzhouBranchofInstituteofSoftware,ChineseAcademyofScience)

Content-adaptiveimagesteganographymeansthatsteganographerchoosessecurityembeddingpositionsbasedonimagetextures.Steganalystcanalsofocusondetectingthesepositionsaccordingtoimagetextures.Gametheoryispreferredtoanalyzetheabovesituation.However,inpreviousgamemodels,steganalystwillmistakenlyidentifythatnobitisembedded,whenthesecretbitisthesameastheleastsignificantbitofcoverimage.Inthispaper,anovelgametheoreticmodelbasedonsecondaryembeddingisproposedtocorrectthejudgmentdrawbackforabetterNashequilibriumbysteganalyst.However,steganalyst’schoicedisturbspreviousequilibriumandsteganographerwillchangehischoicetofindnewequilibriumbyGametheory.Co-occurrencematrixandpointdeviationdegreeareutilizedfordescribingsteganalyst’schoices.Theoccurrencenumberofeachpixelpairsiscalculatedtoconstituteco-occurrencematrix,andthenEuclideandistancebetweenonepointandadjacentpointsiscomputedtolocateembeddingpositions.Incontent-adaptiveimagesteganography,wecandrawaconclusionthatsteganographershouldselectembeddingpositionsfrombothimageedgeareasandsmoothareas.

AFine-grainedAccessControlSchemeforBigDataBasedonClassificationAttributesTengfeiYang(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences),PeisongShen(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences),XueTian(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences),ChiChen(StateKeyLaboratoryofInformationSecurity,InstituteofInformationEngineering,ChineseAcademyofSciences)

Inordertoprotectthesecurityandprivacyofbigdata,thecloudstorageserviceneedstoenforceeffectiveaccesscontrolmechanismonuserrequests.Attribute-BasedEncryptionisapromisingcryptographicaccesscontroltechniquetoensuretheend-to-endsecurityofdataincloud.However,theexistingABEresearchesmainlyfocusontheefficiencydecryption,whiletheflexibilityofpolicy,thecommunicationcost,andthemetadatamanagementofciphertextsarestillchallengingissuesinthebigdataenvironment.Inthispaper,forthefirsttime,weproposeanewdistributed,scalableandfine-grainedaccesscontrolschemebasedonclassificationattributesforthecloudobjectstorage.Theclassificationattributesandthresholdpoliciesareintegratedintoanaccessstructure,andthentheobjectsareencryptedwiththeintegratedaccessstructure.Theconstant-sizeciphertextcomponentsrelatedtoattributescanbemanagedasthecorrespondingmetadata.Asaresulttheencryptioncomplexityandciphertextstoragearereduced.Inaddition,wepresentanewlabel-basedaccesscontrolmodelwithmulti-authoritiestodescribethedetailedrelationshipsofentitiesinourscheme.Besides,theproposedschemeisprovedtobesecureunderlBDHEassumption,andthesystemimplementationdemonstratesthepracticalfeasibilityandgoodperformance.

Social-AwareDecentralizationforEfficientandSecureMulti-PartyComputationYuzheTang(SyracuseUniversity),SuchetaSoundarajan(SyracuseUniversity)

ThisworkstudiestheproblemofMPCschedulingthatis,identifyingasetofcomputingnodestoexecutesecuremulti-partycomputationprotocols(MPC)overadistributedprivatedataset.Ourprimarycontributionisinestimatingtheriskofcollusionbetweennodestowhomthecomputationisscheduled.Thisworkhaspotentialinenablingefficientprivacy-preservingdatasharinginemergingplatformsofbig-datafederation,inhealthcare,finance,andothermarketplaces.Inourmethods,weassumethattheMPCcomputingnodesexistinasocialnetwork,andpresenttwomodelsforestimatingtheriskofcollusion,aswellasalgorithmsforfindingtheMPCnodessuchthattheriskofcollusionisminimized.Weevaluateourmethodsonseveralreal-worldnetworkdatasets,andshowthattheyareeffectiveinminimizingtherisklevels.

StatisticalAnomalyDetectiononMetadataStreamsviaCommoditySoftwaretoProtectCompanyChristineChen(UniversityofPortland),JamesGurganus(MicroSystemsEngineering,Inc.)

Asacompanygrows,itsinfrastructurenaturallymustgrowtosupportit.Theresultingmountainsofinfrastructuremetadatacontainvaluableinformationonthehealthandwellbeingofthesystemsthroughoutthecompany.Forexample,anabnormallylowdiskwriteratetoafileservermayindicatethataregularlyscheduledtaskhasfailedtostart,oranabnormallyhighdiskwriteratemayindicatethepresenceofamaliciousthreatsuchasransomware.Thehypothesisofthiscasestudyisthatsuchmetadatastreamscanbeeffectivelyutilizedbyimplementingstatisticalanomalydetectionmethodsviacommoditysoftware(Splunk,inthiscase).Thesemethodsweretestedprimarilyonservermetadatainaransomwaresimulationandalsoonservermetadatafromfileserversandproductionserversinactiveuse.

Intheransomwaresimulation,thealertingsystemdetectedtheransomwarebehaviorfiveminutesafteranencryptioneventbeganinthesimulationenvironmentandalertedsteadilyforthedurationofthesimulation.Intheweek-longexperimentover11fileserversandproductionservers,atotalof1,484alertsweregenerated.Applyingsimplecorrelationtechniquescreatedamoreconcentratedinformationstreamwith77events.Theseresultsconfirmthevalueofmetadatainidentifyingsystemanomaliesandprovidinganotherlayerofdefenseagainstmaliciousthreats.Therelativelysimpleanomalydetectiontechniquesutilizedinthiscasestudyalsohighlighttheincreasingpracticalityofbehavioralanalytics—itcanonlybeamatteroftimebeforesuchtechniqueswillbeubiquitous.

Computationalimprovementsinparallelizedk-anonymousmicroaggregationoflargedatabasesAhmadMohamadMezher(UniversitatPolitècnicadeCatalunya),AlejandroGarcíaÁlvarez(UniversitatPolitècnicadeCatalunya),DavidRebollo-Monedero(UniversitatPolitècnicadeCatalunya),JordiForné(UniversitatPolitècnicadeCatalunya)

Thetechnicalcontentsofthispaperfallwithinthefieldofstatisticaldisclosurecontrol(SDC),whichconcernsthepostprocessingofthedemographicportionofthestatisticalresultsofsurveyscontainingsensitivepersonalinformation,inordertoeffectivelysafeguardtheanonymityoftheparticipatingrespondents.Theconcretepurposeofthisstudyistoimprovetheefficiencyofawidelyusedalgorithmfork-anonymousmicroaggregation,knownasmaximumdistancetoaveragevector(MDAV),tovastlyaccelerateitsexecutionwithoutaffectingitsexcellentfunctionalperformancewithrespecttocompetingmethods.Theimprovementsputforthinthispaperencompassalgebraicmodificationsandtheuseofthebasiclinearalgebrasubprograms(BLAS)library,fortheefficientparallelcomputationofMDAVonCPU.

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WoSC 2017 Workshop Abstracts

Ripple:HomeAutomationforResearchDataManagementRyanChard(ArgonneNationalLaboratory),KyleChard(UniversityofChicagoandArgonneNationalLab),JasonAlt(NationalCenterforSupercomputingApplications),DilworthParkinson(LawrenceBerkeleyNationalLaboratory),SteveTuecke(UniversityofChicagoandArgonneNationalLab),IanFoster(ArgonneNationalLaboratory&TheUniversityofChicago)

Explodingdatavolumesandacquisitionrates,plusevermorecomplexresearchprocesses,placesignificantstrainonresearchdatamanagementprocesses.Itisincreasinglycommonfordatatoflowthroughpipelinescomprisedofdozensofdif-ferentmanagement,organization,andanalysisstepsdistributedacrossmultipleinstitutionsandstoragesystems.Toalleviatetheresultingcomplexity,weproposeahomeautomationapproachtomanagingdatathroughoutitslifecycle,inwhichusersspecifyviahigh-levelrulestheactionsthatshouldbeperformedondataatdifferenttimesandlocations.Tothisend,wehavedevelopedRIPPLE,aresponsivestoragearchitecturethatallowsuserstoexpressdatamanagementtasksviaarulesnotation.RIPPLEmonitorsstoragesystemsforevents,evaluatesrules,andusesserverlesscomputingtechniquestoexecuteactionsinresponsetotheseevents.WeevaluateoursolutionbyapplyingRIPPLEtothedatalifecyclesoftworeal-worldprojects,inastronomyandlightsourcescience,andshowthatitcanautomatemanymundaneandcumbersomedatamanagementprocesses.

Pipsqueak:LeanLambdaswithLargeLibrariesEdwardOakes(UniversityofWisconsin-Madison),LeonYang(UniversityofWisconsin-Madison),KevinHouck(UniversityofWisconsin-Madison),TylerHarter(MicrosoftGraySystemsLab),AndreaC.Arpaci-Dusseau(UniversityofWisconsin-Madison),RemziH.Arpaci-Dusseau(UniversityofWisconsin-Madison)

Microservicesareusuallyfasttodeploybecauseeachmicroserviceissmall,andthuseachcanbeinstalledandstartedquickly.Unfortunately,leanmicroservicesthatdependonlargelibrarieswillstartslowlyandharmelasticity.Inthispaper,weexplorethechallengesofleanmicroservicesthatrelyonlargelibrariesinthecontextofPythonpackagesandtheOpenLambdaserverlesscomputingplatform.WeanalyzethepackagetypesandcompressibilityoflibrariesdistributedviathePythonPackageIndexandproposePipBench,anewtoolforevaluatingpackagesupport.WealsoproposePipsqueak,apackage-awarecomputeplatformbasedonOpenLambda.

LeveragingtheServerlessArchitectureforSecuringLinuxContainersNiltonBila(IBM),PaoloDettori(IBM),AliKanso(IBM),YujiWatanabe(IBM),AlaaYoussef(IBM)

Linuxcontainerspresentalightweightsolutiontopackageapplicationsintoimagesandinstantiatetheminisolatedenvironments.Suchimagesmayincludevulnerabilitiesthatcanbeexploitedatruntime.Avulnerabilityscanningservicecandetectthesevulnerabilitiesbyperiodicallyscanningthecontainersandtheirimagesforpotentialthreats.Whenathreatisdetected,aneventmaybegeneratedto(1)quarantineorremovethecompromisedcontainer(s)andoptionally(2)remedythevulnerabilitybyrebuildingasecureimage.Webelievethatsuchevent-drivenprocessisagreatfittobeimplementedinaserverlessarchitecture.InthispaperwepresentourdesignandimplementationofaserverlesssecurityanalyticsservicebasedonOpenWhiskandKubernetes.

ServerlessComputing:Design,Implementation,andPerformanceGarrettMcGrath(UniversityofNotreDame),PaulR.Brenner(UniversityofNotreDame)

Wepresentthedesignofanovelperformance-orientedserverlesscomputingplatformimplementedin.NET,deployedinMicrosoftAzure,andutilizingWindowscontainersasfunctionexecutionenvironments.Implementationchallengessuchasfunctionscalingandcontainerdiscovery,lifecycle,andreusearediscussedindetail.WeproposemetricstoevaluatetheexecutionperformanceofserverlessplatformsandconducttestsonourprototypeaswellasAWSLambda,AzureFunctionsandIBM’sdeploymentofApacheOpenWhisk.Ourmeasurementsshowtheprototypeachievinggreaterthroughputthanotherplatformsatmostconcurrencylevels,andweexaminethescalingandinstanceexpirationtrendsintheimplementations.Additionally,wediscussthegapsandlimitationsinourcurrentdesign,proposepossiblesolutions,andhighlightfutureresearch.

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NSF-JST 2017 Workshop Abstracts

AcceleratingBigDataInfrastructureandApplicationsKevinBrown(TokyoInstituteofTechnology),TianqiXu(TokyoInstituteofTechnology),KeitaIwabuchi(TokyoInstituteofTechnology),KentoSato(LawrenceLivermoreNationalLaboratory),AdamMoody(LawrenceLivermoreNationalLaboratory),KathrynMohror(LawrenceLivermoreNationalLaboratory),NikhilJain(LawrenceLivermoreNationalLaboratory),AbhinavBhatele(LawrenceLivermoreNationalLaboratory),MartinSchulz(LawrenceLivermoreNationalLaboratory),RogerPearce(LawrenceLivermoreNationalLaboratory),MayaGokhale(LawrenceLivermoreNationalLaboratory),SatoshiMatsuoka(TokyoInstituteofTechnology)

High-performancecomputing(HPC)systemsareincreasinglybeingusedfordata-intensive,or``BigData",workloads.However,sincetraditionalHPCworkloadsarecompute-intensive,theHPC-BigDataconvergencehascreatedmanychallengeswithoptimizingdatamovementandprocessingonmodernsupercomputers.Ourcollaborativeworkaddressesthesechallengesusingathree-prongedapproach:(i)measuringandmodelingextreme-scaleI/Oworkloads,(ii)designingalow-latency,scalable,on-demandburst-buffersolution,and(iii)optimizinggraphalgorithmsforprocessingBigDataworkloads.Wedescribethethreeareasofourcollaborationandreportontheirrespectivedevelopments.

DisasterNetworkEvolutionUsingDynamicClusteringofTwitterDataKrishnaKant(TempleUniversity),YilangWu(AizuUniversity),ShanshanZhang(TempleUniversity),JunboWang(AizuUniversity),AmitangshuPal(TempleUniversity)

Adhocsmartphonenetworkscanbeusedtoaugmentcommunicationsdegradedbydisastersprovidedthattheindividualadhocclusterscanreachsome``connectiongateways''togetouttotheInternetviaconnecteddevicesinthesurroundingarea(inadditiontoconnectivityviaanyspeciallydeployedemergencyequipment).Thedisconnectedareasarenotknownuntiltheyarebackonline;however,weneedamechanismtodeterminethemsothatthegatewaydevicecanbebestrecruitedtoprovidetheconnectivity.Thisneedstobedoneinadynamicenvironmentbecauseofdisasterrelatedmobility.Inthispaperweproposeamechanismtosolvethisproblembyestimatingregionsthatarelikelytobedensebutdisconnectedwithsignificantnumberofconnecteddevicesaroundthem.Becauseoflackofdirectinformationonpeople(orsmartphone)density,weattempttodothisbyanalyzingthetwitterdata.Byvirtueofitsefficiency,thealgorithmcanbeusedonadynamicallyevolvingdatasetandthusallowsdynamictracking.

Single-epochsupernovaclassificationwithdeepconvolutionalneuralnetworksAkisatoKimura(NTT),IchiroTakahashi(KavliIPMU,TheUniversityofTokyo),MasaomiTanaka(NationalAstronomicalObservatoryofJapan),NaokiYasuda(KavliIPMU,TheUniversityofTokyo),NaonoriUeda(NTT),NaokiYoshida(KavliIPMU,TheUniversityofTokyo)

SupernovaeType-Ia(SNeIa)playasignificantroleinexploringthehistoryoftheexpansionoftheUniverse,sincetheyarethebest-knownstandardcandleswithwhichwecanaccuratelymeasurethedistancetotheobjects.FindinglargesamplesofSNeIaandinvestigatingtheirdetailedcharacteristicshasbecomeanimportantissueincosmologyandastronomy.Thecurrentphotometricsupernovasurveysproducevastlymorecandidatesthancanbefollowedupspectroscopically,highlightingtheneedforeffectiveclassificationmethods.Existingmethodsreliedonaphotometricapproachthatfirstmeasurestheluminanceofsupernovacandidatespreciselyandthenfitstheresultstoaparametricfunctionoftemporalchangesinluminance.However,itinevitablyrequiresalotofobservationsandcomplexluminancemeasurements.Inthiswork,wepresentanovelmethodfordetectingSNeIasimplyfromsingle-shotobservationimageswithoutanycomplexmeasurements,byeffectivelyintegratingthestate-of-the-artcomputervisionmethodologyintothestandardphotometricapproach.Ourmethodfirstbuildsaconvolutionalneuralnetworkforestimatingtheluminanceofsupernovaefromtelescopeimages,andthenconstructsanotherneuralnetworkfortheclassification,wheretheestimatedluminancesandobservationdatesareusedasfeaturesforclassification.BothoftheneuralnetworksareintegratedintoasingledeepneuralnetworktoclassifySNeIadirectlyfromobservationimages.Experimentalresultsshowtheeffectivenessoftheproposedmethodandrevealclassificationperformancecomparabletoexistingphotometricmethodswithmanyobservations.

EnablingLargeScaleDeliberationusingIdeationandNegotiation-SupportAgentsKatsuhideFujita(TokyoUniversityofAgricultureandTechnology),TakayukiIto(NagoyaInstituteofTechnology),MarkKlein(MIT)

ThispaperdescribesanongoingJapan-USprojectthatisdevelopingthekindofadvancedcomputersupportforonlinecrowd-scaledeliberationthatisneededtoenablesmarterandmoreconnectedcommunities.Oursharedworkhasfocusedonaddressingboththeseproblems:(1)ideation:helpingcrowdsmoreeffectivelydeveloppotentialwin-winsolutions,and(2)decision-making:helpingcrowdsgettopareto-optimalityinthesolutionstheyselect.InJapan,adiscussionsupportsystemcalledCOLLAGREEthatfacilitatesfreetextdiscussionstoachieveconsensushasbeendeveloping.InUS,anonlinetoolcalledtheDeliberatoriumthatintegratesargumentationtheoryandsocialcomputingtechniquestoenablemoreeffectivecrowd-scaledeliberationhasbeendeveloping.Oneofourimmediatejointworkistointegratethefacilitatedfree-textdiscussionsofCOLLAGREEwiththestructureddeliberationsprovidedbytheDeliberatorium.Wewillalsodevelopautomatedagentsthatenablebetterideationaswellasbetterdecision-making.

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