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Community Clustering in Distributed Publish/Subscribe System

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IEEE Cluster 2012 Community Clustering in Distributed Publish/Subscribe System Wei Li1,2,Songlin Hu1, Jintao Li1, Hans-Arno Jacobsen3 1 Institute of Computing ... – PowerPoint PPT presentation

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Title: Community Clustering in Distributed Publish/Subscribe System


1
Community Clustering in Distributed
Publish/Subscribe System
IEEE Cluster 2012
  • Wei Li1,2,Songlin Hu1, Jintao Li1, Hans-Arno
    Jacobsen3
  • 1 Institute of Computing Technology, Chinese
    Academy of Sciences
  • 2 Graduate University of Chinese Academy of
    Sciences, Beijing, China
  • 3 University of Toronto, Toronto, Canada

2
Agenda
  • Background
  • Algorithms
  • Experiments
  • Conclusions

3
Background
  • Distributed publish/subscribe systems
  • Clients (publishers subscribers)
  • Routers (a.k.a. brokers)

Distributed Router System
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4
Background
Subscription
Distributed Router System
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5
Background
Subscription
Distributed Router System
Publication
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6
Background
  • Distributed Publish/Subscribe Systems
  • Loosely coupled communication abstraction
  • Widely used in industry, for example
  • GooPS at Google
  • PNUTS at Yahoo!

7
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

8
Algorithms
  • Problem definition
  • Network of interacting clients
  • Distributed routers

9
Algorithms
  • Problem definition contd.
  • The allocation of clients to routers
  • Maximize the performance of the system
  • Minimize the cost of client allocation

10
Agenda
  • Background
  • Algorithms
  • Experiments
  • Conclusions

11
Algorithms
  • Overview

12
Algorithms
  • Steps
  • Phase 1 Network construction among clients
  • Phase 2 Community division of client network
  • Newmans algorithm modularity-based Newman_04

13
Algorithms
  • Steps
  • Phase 3 Heuristic community clustering
  • Majority-place Mp

14
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

15
Agenda
  • Background
  • Algorithms
  • Experiments
  • Conclusions

16
Experiments
  • Community clustering vs. interest clustering
  • Experiment settings
  • Different relationship modes of clients
  • Random
  • Small-world
  • Scale-free
  • Differently structured router overlays

17
Evaluation
  • Different relationship modes among clients
  • Message distribution

18
Evaluation
  • Different relationship modes among clients
  • Message latency load reduction

19
Evaluation
  • Different cluster compositions

20
Agenda
  • Background
  • Algorithms
  • Experiments
  • Conclusions

21
Conclusions
  • A community clustering method is proposed for
    distributed publish/subscribe systems
  • Community clustering is effective to improve the
    performance under different experimental settings
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