middleware for quality-based context distribution in ... · bologna -mario fanelli 25/1/2012...

Middleware for

quality-based context distribution

in mobile systems

January 25th

Ph.D. Student: Mario Fanelli

Tutor: Antonio Corradi

Dipartimento di Elettronica, Informatica e

Sistemistica (DEIS), University of Bologna, Italy

Bologna - 25/1/2012 Mario Fanelli

1. Context-aware Applications for Mobile Systems

i. Context Data Distribution

ii. State-of-the-Art

2. Context Data Distribution Infrastructure

i. Design Guidelines

ii. Distributed System Architecture

iii. Logical Architecture

3. Quality-based Run-time Adaptation

i. Adaptive Query Flooding

4. Cloud-based Context Data Handling

i. VM Placement

ii. Min-Cut Ratio network-aware VM Placement (MCRVMP)

5. Reality Check

6. Conclusions & Ongoing Work

Summary

2


VANET-based scenarios

Smart homes

Emergency response scenarios Delay Tolerant Network

Smart campus

3

Context-aware Applications

for Mobile Systems


Smart City Vision Innovative context-aware services to improve citizenship safety, enhance

traffic scheduling regulation, reduce city energy consumption, etc. Large-scale mobile scenarios, featuring city-wide context data sensing and

distribution tasks Several industrial efforts and EU funded initiatives, such as IBM Smarter

Cities initiative and EU FuturICT project, are currently investigating efficient solutions to build context-aware services in such scenarios

Context-aware Applications

for Mobile Systems

Ad-hoc

Networks

CLOUD

Ad-hoc

Networks

Fixed Wireless Infrastructure

4


Context Data Distribution - Context data have to be represented and distributed to all mobile devices interested in them

We distinguish two principal entity types and one principal service:

Context Data Source - Each source is a producer of context data

Context Data Sink - Each sink is a consumer of context data. It expresses proper context data needs to drive data routing inside the system

Context Data Distribution Infrastructure (CDDI) - The context data distribution infrastructure connects sinks and sources to enable the real context flow into the system

Context Data Distribution

Context Data Distribution Infrastructure

Context Data Source Context Data Sink

Push new

context data

Subscribe for

context data

Notify matching

context data

5


Several CDDIs have been proposed in literature in the past years. Unfortunately they:

focus on local issues, such as context representation & modeling, local context processing, and local service adaptation, e.g., COSMOS [Conan et al. 2007], CoBrA [Chen et al. 2003], MobiPADS [Chan and Chuang 2003]

address only small-/medium-scale deployments, e.g., Gaia [Ranganathan and Campbell 2003], MobiSoC [Gupta et al. 2009], MoCA [Sacramento et al. 2004], Solar [Chen et al. 2008]

rely on either fixed or ad-hoc infrastructure. Only few efforts, e.g., HiCon [Cho et al. 2008], exploit both communication modes to increase system scalability

consider only quality constraints over context data. Simple techniques locally filter context data according to attached quality metadata, e.g., CMF [van Kranenburg et al. 2006], MiddleWhere [Ranganathan et al. 2004], MoCA [Sacramento et al. 2004]

→ Hence, to support the smart cities vision, we need additional research on novel context data distribution infrastructures for large-scale context-aware

mobile scenarios

6

State-of-the-art


1. Heterogeneous wireless communications

Heterogeneous wireless standards to increase both system coverage and total available bandwidth

2. Heterogeneous wireless modes

Wireless infrastructures to ensure context availability and persistency

Wireless ad-hoc communications to reduce the context distribution load

3. CDDI adaptation at run-time

CDDI has to adapt to fit available resources

Introduce differentiated quality levels to drive and constraint possible run-time reconfigurations of the CDDI

Design Guidelines

7


Three-level hierarchical architecture

minimizes tree depth to reduce management overhead

ensures effective usage of wireless infrastructure and ad-hoc modes

Nodes at the same hierarchy level share context data with peers to form cooperative P2P networks

BN1

CN

BN3

BN2

CUN11

CUN21 CUN31

CUN32

SUN111 SUN112

SUN211

SUN321 SUN322

Legend:

CN – Central Node CUN – Coordinator User Node

BN – Base Node SUN – Simple User Node

SUN311

1

2

3

CDDI Distributed Architecture

8


We disseminate context queries to build distribution paths Data flow only on the bottom-up path between the data creator node and the

CN Queries are distributed both on the same level (horizontal distribution)

and to the level above (vertical distribution) Different distribution paths are considered only when matching queries exist

Context Data Distribution Process

BN1

CN

BN3

BN2

CUN11

CUN21 CUN31

CUN32

SUN111 SUN112

SUN211

SUN321 SUN322

Legend:

CN – Central Node CUN – Coordinator User Node

BN – Base Node SUN – Simple User Node

SUN311

Query dissemination Data dissemination

9


Logical Architecture


Context Data Delivery Layer

Context Data Sources Context Data Sinks

Context Data Management Layer R

un

-Tim

e A

dap

tati

on

Su

pp

ort

Representation Processing

Dissemination

Routing Overlay


Context Data Management Layer - context data handling on local node, including representation, storage, and processing operators

Context Data Delivery Layer - coordination and dissemination protocols to carry context data from sources to interested sinks

Run-Time Adaptation Support - dynamic tailoring and management of previous layers depending on current run-time conditions

10


QoC-based

Run-time Adaptation

Context data must be available at the right time, in the right place, and with the right quality

Quality of Context (QoC) expresses quality requirements on both

context data and distribution process Previous QoC definitions focused on the quality of the context data (e.g.,

precision, accuracy, etc.) Delivery phase can negatively affect context-aware adaptations due to high

data droppings, high routing delays, etc.

The Run-time Adaptation Support exploits

QoC constraints, both on context data and on distribution process, to prevent out-of-QoC delivery

monitoring indicators of resources status (e.g., CPU usage, network traffic, etc.) to trigger CDDI adaptations if necessary

In the following, we consider QoC data retrieval time as the maximum time required by the mobile node to access matching context data

11


CDDI uses routing delays at each mobile node to allow neighborhood monitoring, while ensuring QoC data retrieval time Query distribution suppression - Avoid those query distributions that will

hit only nodes that had already received the query Adaptive distribution paths replication - Modify path replication degree at

run-time

→ Differentiated quality levels can reduce the total number of distributed queries over the mobile network

QoC-based Run-time Adaptation:

Adaptive Query Flooding

12

A: QADNL = {A, B, C, D}

B: QADNL = {A, B, C, D}

C: RTC/QADNL = {E}

QADNL = {A, B, C, D}

→ QADNL = {A, B, C, D, E}

D: QADNL = {A, B, C, D}

B

C

Q

A: RTA/QADNL = {B, C, D}

QADNL = {}

→ QADNL = {A, B, C, D}

RTA = {B, C, D}

RTB = {A, C, E}

RTC = {A, B, D, E}

RTD = {A, C, E}

RTE = {B, C, D}

A: QADNL = {A, B, C, D}


B: QADNL = {A, B, C, D}


C: QADNL = {A, B, C, D, E}

D: QADNL = {A, B, C, D}


E: QADNL = {A, B, C, D, E}

A: QADNL = {A, B, C, D, E}

B: QADNL = {A, B, C, D, E}

RTB/QADNL = {}

C: QADNL = {A, B, C, D, E}

D: QADNL = {A, B, C, D, E}

RTD/QADNL = {}

E: QADNL = {A, B, C, D, E}

E A

D


The adaptive approach always distributes fewer queries than normal flooding

Differences more visible for higher request rates and higher TTL since both affect the load perceived by the adaptive solution

Reduced traffic increases the distribution process reliability

Reduced packet droppings

Reduced path breaks due to memory saturation on intermediate mobile devices

0

200000

400000

600000

800000

1000000

1200000

1 2 3 4 Nu

mb

er o

f to

tally

dis

trib

ute

d

qu

eri

es

Query TTL Flooding - 2 reqs/s Adaptive - 2 reqs/s Flooding - 3 reqs/s Adaptive - 3 reqs/s Flooding - 4 reqs/s Adaptive - 4 reqs/s

0,2

0,3

0,4

0,5

0,6

0,7

1 2 3 4

Pe

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tage

of

sati

sfie

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ries

Query TTL Flooding - 2 reqs/s Adaptive - 2 reqs/s Flooding - 3 reqs/s Adaptive - 3 reqs/s Flooding - 4 reqs/s Adaptive - 4 reqs/s

13

QoC-based Run-time Adaptation:

Adaptive Query Flooding


A smart city features thousands of sensors State-of-the-art mobile devices are equipped with onboard sensors Physical environments will include additional sensors, such as temperature

sensors and cameras, feeding new data directly into the fixed infrastructure

Cloud technologies enable the rapid provisioning of scalable services through distributed and virtualized resources On-demand computing resources Dynamic resource scaling depending on user requests

Cloud solutions for CDDI High scalability to address context data storage and processing Dynamic resource scaling lets the CDDI require new computing resources

when the data to be processed increase (due to conference events, etc.)

Cloud solutions require complex VM placement algorithms to decide which VMs should be co-located on the same physical hosts

Cloud-based Context Data Handling

14


Network requirements and constraints have to be considered in the VM placement algorithm to avoid unfeasible placement

Min Cut Ratio Network-Aware VMs Placement (MCRVMP)

Joint work with IBM Haifa Research Lab, Israel

Optimization goal: Minimize the maximum cut load value to increase network elasticity, in order to increase the probability that, if traffic demands change, the network is still able to accommodate them

MCRVMP on a tree network

The removal of each link leads to a network cut

MCRVMP checks that, for each link, the total traffic flowing from VMs placed in one side to VMs placed in the other is lower than the link capacity

Min Cut Ratio

Network-Aware VMs Placement

15

Core

switch

Aggregation

switches

ToR

Hosts

Complex network topologies

not shown for time reason


In December 2009, mobile data traffic overcome voice traffic on a global basis and is expected to double annually for the next five years

→ It becomes important to offload wireless infrastructure by ad-hoc communications and data caching on mobile nodes

Telco operators look at context-aware applications as appealing and capable of attracting more customers (several industrial initiatives and EU projects in this area)

→ It is fundamental to understand the quality requirements of context-aware applications and to correctly manage context delivery process

Several vendors (e.g., Amazon, Google, IBM, etc.) exploits Cloud solutions to efficiently use their own data center

→ VM placement algorithms are fundamental to enable the Cloud vision, so to avoid resource shortage and prevent performance bottlenecks

CDDI: Reality Check

16


To conclude:

Context-awareness is one of the core capability in next generation mobile networks

Even if context-awareness has been widely study in the last two decades, the realization of context-aware scenarios in wide-scale networks is still an open problem due to the high management overhead introduced by context data distribution

We proposed a hierarchical architectural model with associated context data distribution and memorization protocols that strives to offload fixed wireless infrastructures by context data distribution burden

At the current stage, our CDDI is able to use mixed wireless standards and modes, heterogeneous wireless standards, distributed cooperative caching, …

However, new interesting research directions

are still open and to be pursued…

Conclusions

17


The integration of Cloud solutions unlocks new interesting scenarios, where the CDDI dynamically requires and releases computational resources at run-time

• The fixed infrastructure can monitor different context aspects of the mobile infrastructure

Mobility, context data requests, QoC constraints, etc.

Social networks, inferred by node mobility, online networking services (e.g., Facebook and Linkedin), etc.

Ongoing research direction → Design and development of a self-adaptive CDDI capable of dynamically selecting and applying the most suitable distribution techniques

Pros: System scalability

Cons: Monitoring overhead, CDDI increased complexity, system instability

18

Ongoing Work


Thanks for your attention!

Questions

19


Publications

INTERNATIONAL JOURNAL AND MAGAZINES (ACCEPTED)

A Survey of Context Data Distribution for Mobile Ubiquitous Systems, P. Bellavista, A. Corradi, M.

Fanelli, L. Foschini, Accepted in ACM Computing Surveys (CSUR), ACM Press, expected to appear in

Vol. 45, No. 1, Mar 2013, pages 1-49.

Towards Adaptive and Scalable Context-Aware Middleware, A. Corradi, M. Fanelli, L. Foschini,

Invited article in International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS), 1(2010),

IGI-Global Press.

INTERNATIONAL CONFERENCES (ACCEPTED & PUBLISHED) – 1/2

Context Data Distribution in Mobile Systems: a Case Study on Android-based Phones, M. Fanelli,

L. Foschini, A. Corradi, A. Boukerche, Accepted for publication in the Proceedings of the IEEE

International Conference on Communications (ICC '12), Ottawa, Canada, June, 2012, IEEE Computer

Society Press.

Resource-Awareness in Context Data Distribution for Mobile Environments, M. Fanelli, L.

Foschini, A. Corradi, A. Boukerche, Proceedings of the IEEE Global Communications Conference

(GLOBECOM’11), Houston, Texas, USA, Dec. 5-9, 2011, IEEE Computer Society Press

QoC-based Context Data Caching for Disaster Area Scenarios, M. Fanelli, L. Foschini, A. Corradi,

A. Boukerche, Proceedings of the IEEE International Conference on Communications (ICC '11), Kyoto,

Japan, July, 2011, IEEE Computer Society Press.

Counteracting wireless congestion in data distribution with adaptive batching techniques, M.

Fanelli, L. Foschini, A. Corradi, A. Boukerche, Proceedings of the IEEE Global Communications

Conference (GLOBECOM’10), Miami, Florida, USA, Dec. 6-10, 2010, IEEE Computer Society Press.

20


Publications

INTERNATIONAL CONFERENCES (ACCEPTED & PUBLISHED) – 2/2

Self-Adaptive and Time-Constrained Data Distribution Paths for Emergency Response

Scenarios, M. Fanelli, L. Foschini, A. Corradi, A. Boukerche, Proceedings of the 8th ACM Symposium

on Mobility Management and Wireless Access (MOBIWAC’10), Bodrum, Turkey, Oct. 17-21, 2010, ACM

Press.

Towards Efficient and Reliable Context Data Distribution in Disaster Area Scenarios, M. Fanelli, L.

Foschini, A. Corradi, A. Boukerche, Short paper in the Proceedings of the 35th IEEE Conference on

Local Computer Networks (LCN’10), Denver, Colorado, USA, Oct. 11-14, 2010, IEEE Computer Society

Press.

Adaptive Context Data Distribution with Guaranteed Quality for Mobile Environments, A. Corradi,

M. Fanelli, L. Foschini, Proceedings of the IEEE International Symposium on Wireless Pervasive

Computing (ISWPC'10), Modena, Italy, May 5-7, 2010, IEEE Computer Society Press.

Implementing a Scalable Context-Aware Middleware, A. Corradi, M. Fanelli, L. Foschini,

Proceedings of the 14th IEEE International Symposium on Computers and Communications (ISCC'09),

Sousse, Tunisia, Jul. 5-8, 2009, IEEE Computer Society Press.

INTERNATIONAL WORKSHOPS (ACCEPTED & PUBLISHED)

Increasing Cloud Power Efficiency through Consolidation Techniques, A. Corradi, M. Fanelli, L.

Foschini, Proceedings of the IEEE Workshop on Management of Cloud Systems (MoCS 2011), Kerkyra

(Corfu), Greece, June 28, 2011, IEEE Computer Society Press.

21


22

Collected Credits

Academic Courses:

Applicazioni di Intelligenza Artificiale L-S, held by Prof. Paola Mello in the degree course of

Ingegneria Informatica. (60 CFU)

Sistemi Distribuiti L-S, held by Prof. Paolo Bellavista in the degree course of Ingegneria Informatica.

(60 CFU)

Short Courses:

“Short-Range Positioning Systems: Fundamentals and Advanced Research Results with Case

Studies”, organized by Prof. Davide Dardari and Prof. Andrea Conti. (8 CFU)

“Heterogeneous wireless networks architectures, Qos performance and applications”, organized

by Prof. Oreste Andrisano. (12 CFU)

“Infrastructure and Support to Wireless Systems”, organized by Prof. Antonio Corradi. (12 CFU)

Winter School:

Middleware for Network Eccentric and Mobile Applications (MINEMA) 2009, winter school in

Goteborg (Sweden), 23-26 March 2009. (19.5 CFU)

Abroad Stay:

February 2010 – July 2010, Ph.D. Visiting Student at the PARADISE Research Lab, working under the

supervision of Prof. Azzedine Boukerche, School of Information Technology and Engineering (SITE),

University of Ottawa, Canada. (60 CFU)

• June 2011 – September 2011, Internship @ IBM Haifa Research Lab, Haifa, Israel. (0 CFU due to

PhD regulations)

In total: 231.5

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