elastic processes on clouds of hybrid services: principles, enabling techniques and approaches
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SummerSOC 12, 3 Jul 2012 1
Elastic Processes on Clouds of Hybrid Services: Principles, Enabling Techniques
and Approaches
Hong-Linh Truong Distributed Systems Group,
Vienna University of Technology
truong@infosys.tuwien.ac.athttp://www.infosys.tuwien.ac.at/staff/truong
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Acknowledgement: some results from joint works with: Kamal Bhattacharya, Schahram Dustdar, Muhammad Zuhri Catur Candra, Aditya Ghose, Lam-Son Le, Frank Leymann, Vitaliy Liptchinsky, Tran-Vu Pham, Michael Reiter, Mirela Riveni, Ognjen Scekic, Quang-Hieu Vu, and Rostyslav Zaboloznyi
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An example of elastic processes
Recap: Schahram‘s presentation yesterday
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Hybrid services in the cloud
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How to realize elasticity in hybrid systems?
Attention: ongoing work not everything is well tested !
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Outline
Vienna Elastic Computing Model Incorporating humans into programming
paradigms Programming hybrid services in the cloud Modeling elasticity constraints Reasoning elasticity constraints Conclusions and future work
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VIENNA ELASTIC COMPUTING MODEL
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Multi-dimensional Elasticity
Units can be networks, middleware, SaaS, PaaS
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Vienna Elastic Computing Modelmulti-dimensional elasticity, service computing models and cloud provisioning models
Schahram Dustdar, Hong-Linh Truong, "Virtualizing Software and Humans for Elastic Processes in Multiple Clouds - a Service Management Perspective" International Journal of Next-Generation Computing, To appear, 2012
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INCORPORATING HUMANS INTO PROGRAMMING PARADIGM
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Incorporate humans into a programming paradigm
• Abstracting human compute units as program elements• Extending programming languages to support human
compute units• Data/control flows via extensible APIs
Programming languages
• Shared memory (e.g., human –software – human), message passing (human-to-human), artifact-centric, etc., via APIs working atop the compute unit abstraction layer
Multiple programming
models
• Computing capability /profile management: human computing power, reputation and incentive models
• Monitoring: monitoring and enforcing incentives/rewards, quality of results, availability
• Communication: between human-middleware, among ICU/SCU , within SCU for exchanging artifacts and comprehensing social tasks
Execution environment
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Incorporate humans into a programming paradigm
Volunteers ProfessionalsTeamIndividual
Service-based Middleware
CommunicationMonitoringCapability/Profile
Management
Provisioning/Negotiation/Execution API
Abstraction of Human Compute Units
SCU
ICU
ICU ICU SCU
Program languages and programming models
Program elements
Software Compute
Units
program human actions and dependenciesprogram incentive condition and rewarding actionprogram result evaluation method
Human-to-middleware interfaces:• visualization of collective
tasks• embedding of common
forms• mobile app
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Existing technologies are not adequate
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PROGRAMMING HYBRID SERVICES IN THE CLOUD
Hong-Linh Truong, Schahram Dustdar, Kamal Bhattacharya, Programming Hybrid Services in the Cloud, Working paper,2012
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HBS Communication Interface
Based on well known technologies for integrating human requests/responses into SBS Emails, web interfaces, Web services, etc.
Some special characteristics The same protocol can be used for multiple layers
(middleware, application, OS) -- unlike in SBS Only asyschronous communication and unknown
upper-bound delay
Can support message passing and shared memory models hbsi hbsj or hbsi sbs hbsj
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Human Power Unit (HPU)
Humans are determined via skills/skill levels A particular HBS cloud can define its own sets of
skills (CS) and skill levels (SK) Similar to Amazon defines its own EC unit Different ways to make sure skill and skill levels
declared in a cloud consistent (e.g., via testing and monitoring).
HPU Definition: HPU is a value describing the computing power of an HBS measured in an abstract unit. A cloud of HBS has a pre-defined basic power unit, hpuθ, corresponding to the baseline skill bsθ of the cloud.”
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Human Power Unit (HPU) – our starting point
Given an HBS, its theoretical HPU can be larger or smaller than its real HPU – unlike SBS
HPU for a particular (skill, skill level)
HPU for a set of (skill, skill level), no weighted factors
Decomposition/composition of HPU for a shared HBS
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HBS Instances Provisioning
• Individual Compute Unit (ICU)• Social Compute Unit (SCU
Types of services
• iICU(CS, HPU, price, utilization,location, APIs)
• iSCU(CS,HPU, price, utilization,connectedness, location, APIs)
• Other (traditional) NFPs
Instances Description
s
• utilization• offering communication APIs• connectedness
Pricing factors
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Cloud APIs for Provisioning Hybrid Services
APIs hide low-level platforms and utilize low level HBS communication interfaces
APIs for HBS information and management
•listSkills();listSkillLevels();•listICU();listSCU()•negotiateHBS()•startHBS()•suspendHBS ()•resumeHBS ()•stopHBS()•reduceHBS()•expandHBS()
APIs for HBS execution and
communication •runRequestOnHBS ()•receiveResultFromHBS()
•sendMessageToHBS() •receiveMessageFromHBS()
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Prototype (simulated environment)
Combined with Jcloud/boto for real SBS
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Framework for Utilizing Hybrid Services
IT ManagementSmart City Management
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Modeling HPU-aware Task Dependency Graphs
Link management skills and compliance constraints to tasks required HBS
Can be specified in TOSCA!Modeled in dependency graphs
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Programming a combination of HBS and SBS
e.g., preparing/managing inputs/outputs for HBS using SBS
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Forming iSCUs Done by consumers or cloud providers
SCU Formation Algorithms
Business As Usual
Corrective Action
HBS Constraints
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Configuring iSCU
Establish „connectedness“ based on compliance constraints and network topology Addional cost might occur!
Program SBS and HBS for the iSCU to have a complete working environment.
Different connectedness E.g., Ring-based, star-based, and master-slave
topologies
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Selecting HBS: Some algorithms
Several algorithms can be built based on existing team formation algorithms which do not consider dependency graphs
Different weighted factors can be consideredSummerSOC 12, 3 Jul 2012
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Forming iSCU by minimizing cost and considering no direction
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Example of star-based iSCU using Dropbox as a communication hub
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Change Model for Task Graph’s Human Power Unit
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MODELING ELASTICITY CONSTRAINT
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Elasticitiy modeling
• Beyond traditional QoS metrics and for only services
• Modeling elasticity constraints• Attaching elasticity constraints to processes at
the design time for composition and runtime elasticity
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Beyond QoS metrics (1)
And many more metrics!
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Beyond QoS metrics (2)
Data marketplaces and DaaSElastic dynamic properties associated with:Service APIs, data APIs and data assets
Quang Hieu Vu, Tran Vu Pham, Hong-Linh Truong, Schahram Dustdar, Rasool Asal, DEMODS: A Description Model for Data-as-a-Service, The 26th IEEE International Conference on Advanced Information Networking and Applications (AINA-2012)
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Elastic Profile Using Rules
process
process fragment
activity
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Declarative constraints based first-order logic and set theory
Lam-Son Le, Hong-Linh Truong, Adytia Ghose, Schahram Dustdar, On Elasticity and Constrainedness of Business Services Provisioning, 9th International Conference on Services Computing (SCC 2012)
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REASON THE ELASTICITY CONSTRAINTS
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Runtime quality, cost and benefits evaluation and reasoning principles
Part A Part B Part C
model i model j model k
Runtime: Elastic processes
Quality, cost and benefits
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Composable cost evaluation
Hong Linh Truong, Schahram Dustdar: Composable cost estimation and monitoring for computational applications in cloud computing environments. Procedia CS 1(1): 2175-2184 (2010)
Tran Vu Pham, Hong-Linh Truong, Schahram Dustdar "Elastic High Performance Applications - A Composition Framework", The 2011 Asia-Pacific Services Computing Conference (IEEE APSCC 2011), (c) IEEE Computer Society, December 12 - 15, 2011, Jeju, Korea
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Runtime evaluation of quality of data in workflows
Michael Reiter, Uwe Breitenbuecher, Schahram Dustdar, Dimka Karastoyanova, Frank Leymann, Hong-Linh Truong, A Novel Framework for Monitoring and Analyzing Quality of Data in Simulation Workflows, (c)IEEE Computer Society, The 7th IEEE International Conference on e-Science, 5-8 December, 2011, Stockholm, SwedenMichael Reiter, Hong-Linh Truong, Schahram Dustdar, Dimka Karastoyanova, Robert Krause, Frank Leymann, Dieter Pahr, On Analyzing Quality of Data Infuences on Performance of Finite Elements driven Computational Simulations, EuroPar 2012
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Incentive Monitoring
Ognjen Scekic, Hong-Linh Truong, Schahram Dustdar, "Modeling Rewards and Incentive Mechanisms for Social BPM",, Short paper, 10th International Conference on Business Process Management (BPM2012), (c)Springer-Verlag, September 3-6, 2012, Tallinn, Estonia
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Put things together -- IDE for elastic processes
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Put things together – from high-level languages to programmable units
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Statelet: a declarative coordination language for collaboration processes
Statelet statements can be mapped to APIs for programmable units
Vitaliy Liptchinsky, Roman Khazankin, Hong Linh Truong, Schahram Dustdar: Statelets: Coordination of Social Collaboration Processes. COORDINATION 2012
Provisioning/Negotiation/Execution API
Abstraction of Human Compute Units
SCUICUICU ICU SCU
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Conclusions and Future Work
Novel models and APIs are needed for integrating humans into program paradigms
We need to address the whole stack Elasticity modeling, monitoring and reasoning Mapping high level languages to APIs of hybrid services
Future works Several issues mentioned here are ongoing work Declarative languages for specifying elasticity Methods for evaluation and experiments
Some open questions will be addressed in FP7 CELAR
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Thanks for your attention
Hong-Linh TruongDistributed Systems GroupVienna University of Technologytruong@infosys.tuwien.ac.athttp://www.infosys.tuwien.ac.at/staff/truong
SummerSOC 12, 3 Jul 2012
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