Community Clustering in Distributed Publish/Subscribe System Wei Li 1,2,Songlin Hu 1, Jintao Li 1,...
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Transcript of Community Clustering in Distributed Publish/Subscribe System Wei Li 1,2,Songlin Hu 1, Jintao Li 1,...
Community Clustering in Distributed Publish/Subscribe SystemWei Li1,2,Songlin Hu1, Jintao Li1, Hans-Arno Jacobsen3
1 Institute of Computing Technology, Chinese Academy of Sciences2 Graduate University of Chinese Academy of Sciences, Beijing, China3 University of Toronto, Toronto, Canada
IEEE Cluster 2012
Agenda
Background Algorithms Experiments Conclusions
Background
Distributed publish/subscribe systems Clients (publishers & subscribers) Routers (a.k.a. brokers)
Publisher
Publisher
…
Distributed Router SystemSubscriber
Subscriber
…
Broker Broker
BrokerBroker
BrokerAdvertisement
Background
Publisher
Publisher
…
Distributed Router SystemSubscriber
Subscriber
…
Broker Broker
BrokerBroker
Broker
Subscription
Advertisement
Background
Publisher
Publisher
…
Distributed Router SystemSubscriber
Subscriber
…
Broker Broker
BrokerBroker
BrokerAdvertisement
Subscription
Publication
Background
Distributed Publish/Subscribe Systems Loosely coupled communication abstraction Widely used in industry, for example
GooPS at Google PNUTS at Yahoo!
Client Placement Client placement affects performance of the
system Current solutions
Connecting to closest broker [Chen_05] Interest clustering of subscribers [Querzoni_08,
Riabov_02] Publisher dynamic placement [Cheung_10]
Limitations Complex communication relationships in interacting clients
are not considered The cost of client relocation is not considered
Algorithms
Problem definition Network of interacting clients
Distributed routers
Cluster1Cluster2 Cluster3 Cluster4 Cluster5
Algorithms
Problem definition cont’d. The allocation of clients to routers
Maximize the performance of the system Minimize the cost of client allocation
Agenda
Background Algorithms Experiments Conclusions
Algorithms
Overview
Algorithms
Steps Phase 1: Network construction among clients Phase 2: Community division of client network
Newman’s algorithm: modularity-based [Newman_04]
Algorithms
Steps Phase 3: Heuristic community clustering
Majority-place Mp:
Algorithms
Steps Phase 2 and Phase 3 are iterative: Re-divide
several communities into smaller ones Performance lose vs. deployment cost decrease Achieve trade off between performance and deployment
cost Phase 4: Load balancing
Agenda
Background Algorithms Experiments Conclusions
Experiments
Community clustering vs. interest clustering Experiment settings
Different relationship modes of clients Random Small-world Scale-free
Differently structured router overlays
Evaluation
Different relationship modes among clients Message distribution
Evaluation
Different relationship modes among clients Message latency & load reduction
Evaluation
Different cluster compositions
Agenda
Background Algorithms Experiments Conclusions
Conclusions
A community clustering method is proposed for distributed publish/subscribe systems
Community clustering is effective to improve the performance under different experimental settings