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Pervasive Social Computing : Algorithms andDeployments
Sonia Ben Mokhtar
Mobisys Seminar
28th October 2008
HeterogeneousPlatforms
Pervasive functionalities
10
Middleware for pervasive computing
Social Networks
Pervasive Social Computing (PSC)
ScenariosArrange a tennis gamearound the campus at
5pm, intermediate playerlevel, 1hour
I would like to have acoffee with otherbreastfeeding mums inmy neighborhood
I am looking for apostdoc/internship in anEnglish speakingcountry starting nextSeptember
I would like to sharea cab for going back
home
Issues and Key Challenges
• Issues– Environment Heterogeneity & Dynamics– Social-based user centrism– Distributed, Multi-activity social network– Context-awareness– Semantic-awareness– Privacy
• Challenges– Middleware hosted in some (all) devices responsible for :
• Specification of user tasks (social-, semantic-aware)• Disseminating user tasks (scalable, privacy- and context-aware)• Matching user tasks (social-, semantic- and context-aware)• Notifying the users of matching results
Related Work: Existing MiddlewareParadigms
• Tuple Space (distributed shared memory)– Stateful, non-scalable
• Event-based (pub-sub)– Proactive– Persistency of subscriptions vs volatility of user tasks
• RPC-based (SOC)– Persistency of services vs volatility of user tasks– Service requester and provider roles are merged
Outline
• A Middleware for PSC (Overview)• Matching User Task Specifications
– Algorithms & Evaluation• Middleware Deployment Strategies
– Deployment Strategies & Evaluation• Conclusions & Future Work
A Middleware for PSC (Overview)
User Task Specification
UserId: totoActivity: TennisNbrPersons: 1Preferences: -A-->0.8 -B-->0.5 -C-->0.2Context Properties:5pm, UCL campus, 2hoursTTL = 2hours from now
Outline
• A Middleware for PSC (Overview)• Matching User Task Specifications
– Algorithms & Evaluation• Middleware Deployment Strategies
– Deployment Strategies & Evaluation• Conclusions & Future Work
Matching user tasks in PSC
T1 T1 T2 Tn…
Return Ti: Max(Utility(T,Ti))
Overall Satisfaction
T1 T2 Tn…
1. Generate all the possible pairs2. Compute Best CombinationC*={(Ti,Tj)}: Utility(C*)=Max3. Return Ti associated with T
T? T?
T? T? T1 T2 Tn…
1. Generate all the possible pairs2. Generate a Combination of the BestpairsC+= {(Ti,Tj)}: Utility((Ti,Tj)) >Utility((Ti+1,Tj+1))3. Return Ti associated with T
Local SatisfactionFIFO
Nearly Overall
Return T1
Evaluation of the Matching Strategies
• Mobility Traces: MIT• Social Network: Advogato• Scenario:
– A node is elected to act as a broker (most popular)– Each time a node encounters the broker: Task Publication– When a node meets the broker again it is notified of the answer if any
(matching, expiry)• Measurements:
– Accuracy (generated utility, distribution of the utility)– Computational Overhead, delay to answer
Utility wrt Matching Strategy
Distribution of the Satisfaction wrt MatchingStrategy
Overhead of the Matching Strategies
Delay to Answer wrt Matching Strategy
Matching Strategy: Discussion
• Resource Constraints and Social Network is notimportantFifo
• Resource Constraints and Social Network iscriticalLocal
• OtherwiseNearly or Combined
Outline
• A Middleware for PSC (Overview)• Matching User Task Specifications
– Algorithms & Evaluation• Middleware Deployment Strategies
– Deployment Strategies & Evaluation• Conclusions & Future Work
Middleware Deployment Strategies:Stationary Highly Connected Overlay
Middleware Deployment Strategies:Mobile Loosely Connected Overlay
Publication T1
Publication T2
Dissemination T1,T2Matching T1,T2
Notification T2 Notification T1
Middleware Deployment Strategies:Mobile Independent Brokers
Evaluation of the Deployment Strategies
• Mobility Traces: MIT, Cambridge• Social Network: Advogato, MovieLens, LastFM• Scenario:
– N brokers are elected in the Network (popularity)– Each time a node encounters a brokers: Task Publication– When a node meets the broker again it is notified of the answer if
any (matching, expiry)• Measurements:
– Accuracy (distribution of utility wrt: strategy, number ofbrokers,Traces)
– Communication Overhead (Number of messages, amount oftraffic)
Distribution of the Satisfaction wrtDeployment Strategy
Communication Overhead of the DeploymentStrategies
Distribution of the Satisfaction wrt Number ofBrokers and Mobility Traces
Middleware Deployment Strategies:Discussion
• If an infrastructure exists (e.g., campus)Stationary Overlay
• If no infrastructure and setting up one does notworth it (e.g., conference)Mobile OverlayA pre-analysis is worth doing to estimate the number of
brokers to deploy
Effect of the Connectivity of the SocialNetwork on the Utility
Outline
• A Middleware for PSC (Overview)• Matching User Task Specifications
– Algorithms & Evaluation• Middleware Deployment Strategies
– Deployment Strategies & Evaluation• Conclusions & Future Work
Conclusions & FW
• Pervasive Social Computing– Enable social interactivity among mobile users– Middleware for PSC support the scalable task
publication/dissemination/notification, social-context-aware taskmatching
• FW: The propagation of the social preferences by thebrokers– In the same activity, across different activities
• FW: Semantic specification and matching of user tasks– Emergent semantics vs Ontology-based approach