1. Community Clustering in Distributed Publish/Subscribe System Wei Li 1,2,Songlin Hu 1, Jintao Li 1, Hans-Arno Jacobsen 3 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 IEEE Cluster 2012

2. Agenda Background Algorithms Experiments Conclusions

3. Background Distributed publish/subscribe systems  Clients (publishers & subscribers)  Routers (a.k.a. brokers) … Distributed Router System … Advertisement

4. Background … Distributed Router System … Subscription Advertisement

5. Background … Distributed Router System … Advertisement Subscription Publication

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 cont’d.  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 Newman’s 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. A community clustering method is proposed for distributed publish/subscribe systems Community clustering is effective to improve the performance under different experimental settings