Journal of Computing Science and Engineering

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지
DOI QR코드

DOI QR Code

DIMPLE-II: Dynamic Membership Protocol for Epidemic Protocols

  • Sun, Jin (Department of Computer Science Michigan Technological University) ;
  • Choi, Byung-K. (Department of Computer Science Michigan Technological University) ;
  • Jung, Kwang-Mo (Korea Electronics Technology Institute)
  • Published : 2008.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

Epidemic protocols have two fundamental assumptions. One is the availability of a mechanism that provides each node with a set of log(N) (fanout) nodes to gossip with at each cycle. The other is that the network size N is known to all member nodes. While it may be trivial to support these assumptions in small systems, it is a challenge to realize them in large open dynamic systems, such as peer-to-peer (P2P) systems. Technically, since the most fundamental parameter of epidemic protocols is log(N), without knowing the system size, the protocols will be limited. Further, since the network churn, frequently observed in P2P systems, causes rapid membership changes, providing a different set of log(N) at each cycle is a difficult problem. In order to support the assumptions, the fanout nodes should be selected randomly and uniformly from the entire membership. This paper investigates one possible solution which addresses both problems; providing at each cycle a different set of log(N) nodes selected randomly and uniformly from the entire network under churn, and estimating the dynamic network size in the number of nodes. This solution improves the previously developed distributed algorithm called Shuffle to deal with churn, and utilizes the Shuffle infrastructure to estimate the dynamic network size. The effectiveness of the proposed solution is evaluated by simulation. According to the simulation results, the proposed algorithms successfully handle network churn in providing random log(N0 fanout nodes, and practically and accurately estimate the network size. Overall, this work provides insights in designing epidemic protocols for large scale open dynamic systems, where the protocols behave autonomically.

Keywords

References

  1. AGUILERA, M., STROM, R., STURMAN, D., ASTLEY, M., and CHANDRA, T. 1999. Matching events in a content-based subscription system. In Proceedings of the 18th ACM Symposium on Principles of Distributed Computing (PODC '99). ACM, Atlanta, GA, 53-61.
  2. ALLAVENA, A., DEMERS, A., AND HOPCROFT, J. 2005. Correctness of a gossip based membership protocol. In Proceedings of the 24th ACM Symposium on Principles of Distributed Computing (PODC '05). ACM, Las Vegas, NV, 292-301.
  3. BANSOD, N., MALGI, A., CHOI, B., AND MAYO, J. 2005. Muon: Epidemic based mutual anonymity. In Proceedings of the 13th International Conference on Network Protocols (ICNP '05). IEEE, Boston, MA, 99-109.
  4. BIRMAN, K., HAYDEN, M., OZKASAP, O., XIAO, Z., BUDIU, M., AND MINSKY, Y. 1999. Bimodal multicast. ACM Transactions on Computer Science 17(2):41-88. https://doi.org/10.1145/312203.312207
  5. BOLOT, J.-C., TURLETTI, T., AND WAKEMAN, I. 1994. Scalable feedback control for multicast video distribution in the internet. SIGCOMM Comput. Commun. Rev. 24(4):58-67. https://doi.org/10.1145/190809.190320
  6. CARZANIGA, A. 1998. Architectures for an event notification service scalable to wide-area networks. Ph.D. thesis, Politecnico di Milano.
  7. CUENCA-ACUNA, F., PEERY, C., MARTIN, R., AND NGUYEN, T. 2003. Planetp: Using gossiping to build content addressable peer-to-peer information sharing communities. In Proceedings of the 12th International Symposium on High Performance Distributed Computing (HPDC '03). IEEE, Seattle, WA, 236-246.
  8. DARROCH, J. 1958. The multiple-recapture census: I. estimation of a closed population. Biometrika 45, 343-359.
  9. DAS, A., GUPTA, I., AND MOTIVALA, A. 2002. Swim: Scalable weakly-consistent infection-style process group membership protocol. In International Conference on Dependable Systems and Networks (DSN '02). IEEE, Bethesda, MD, 303-312.
  10. DEMERS, A., GREENE, D., HOUSER, C., IRISH, W., LARSON, J., SHENKER, S., STURGIS, H., SWINEHART, D., AND TERRY, D. 1987. Epidemic algorithms for replicated database maintenance. In Proceedings of the 6th Annual ACM Symposium on Principles of Distributed Computing (PODC '87). ACM, Vancouver, British Columbia, Canada, 1-12.
  11. EUGSTER, P., GUERRAOUI, R., KERMARREC, A.-M., AND MASSOULIE, L. 2004. From epidemics to distributed computing. IEEE Computer 37(5):60-67.
  12. EUGSTER, P., HANDURUKANDE, S., GUERRAOUI, R., KERMARREC, A.-M., AND KOUZNETSOV, P. 2001. Lightweight probabilistic broadcast. In Proceedings of the International Conference on Dependable Systems and Networks (DSN '01). IEEE, Goteborg, Sweden, 443-452.
  13. GANESH, A.-J., KERMARREC, A.-M., AND MASSOULIE, L. 2003. Peer-to-peer membership management for gossip-based protocols. IEEE Transactions on Computers 52(2):139-149. https://doi.org/10.1109/TC.2003.1176982
  14. GKANTSIDIS, C., MIHAIL, M., AND SABERI, A. 2004. Random walks in peer-to-peer networks.
  15. Gupta, I., Birman, K., and Resesse, R. 2002. Fighting fire with fire: Using randomized gossip to combat stochastic scalability limits. Journal on Quality and Reliability Engineering International 29(8):165-184.
  16. HOROWITZ, K. AND MALKHI, D. 2003. Estimating network size from local information. Inf. Process. Lett. 88(5):237-243. https://doi.org/10.1016/j.ipl.2003.08.011
  17. JELASITY, M., GUERRAOUI, R., KERMARREC, A.-M., AND VAN STEEN, M. 2004. The peer sampling service: Experimental evaluation of unstructured gossip-based implementations. In Proceedings of the 5th ACM/IFIP/USENIX International Conference on Middleware. ACM, Toronto, Ontario, Canada, 79-98.
  18. JELASITY, M., KOWALCZYK, W., AND VAN STEEN, M. 2003. Newscast computing. Technical Report IR-CS-006, Vrije Universiteit Amsterdam, Department of Computer Science.
  19. KOSTOULAS, D., PSALTOULIS, D., GUPTA, I., BIRMAN, K., AND DEMERS, A. 2005. Decentralized schemes for size estimation in large and dynamic groups. In 4th IEEE International Symposium on Network Computing and Applications (NCA), 41-48.
  20. LV, Q., CAO, P., COHEN, E., LI, K., AND SHENKER, S. 2002. Search and replication in unstructured peer-to-peer networks. In 16th ACM International Conference on Supercomputing (ICS '02). ACM, New York, NY, 84-95.
  21. MANE, S., MOPURU, S., MEHRA, K., AND SRIVASTAVA, J. 2005. Network size estimation in a peertopeer network. Technical Report 05-030, University of Minnesota, Department of Computer Science and Engineering.
  22. MANKU, G. S. 2003. Routing networks for distributed hash tables. In PODC'03: Proceedings of the twenty-second annual symposium on Principles of distributed computing. ACM Press, New York, NY, USA, 133-142.
  23. MASSOULIE, L., MERRER, E. L., KERMARREC, A.-M., AND GANESH, A. 2006. Peer counting and sampling in overlay networks: Random walk methods. In International Conference on Principles Of Distributed Computing (PODC'06). ACM, Denver, CO, 123-132.
  24. PORTMANN, M. AND SENEVIRATNE, A. 2003. Cost-effective broadcast for fully decentralized peertopeer networks. Journal of Computer Communications 26(11):1159-1167. https://doi.org/10.1016/S0140-3664(02)00250-5
  25. PSALTOULIS, D., KOSTOULAS, D., GUPTA, I., BIRMAN, K., AND DEMERS, A. 2004. Practical algorithms for size estimation in large and dynamic groups. In PODC'04: Proceedings of the twenty-third annual symposium on Principles of distributed computing. ACM, New Foundland, Canada, 25-28.
  26. RHEA, S., GEELS, D., ROSCOE, T., AND KUBIATOWICZ, J. 2004. Handling Churn in a DHT. In Proceedings of the 2004 USENIX Technical Conference, Boston, MA, USA. USENIX, citeseer. csail.mit.edu/article/rhea04handling.html.
  27. STAVROU, A., RUBENSTEIN, D., AND SAHU, S. 2004. A lightweight, robust p2p system to handle flash crowds. IEEE Journal on Selected Areas in Communications 22(7):6-17. https://doi.org/10.1109/JSAC.2003.818778
  28. STUTZBACH, D. AND REJAIE, R. 2004. Towards a better understanding of churn in peer-to-peer networks. Technical Report CIS-TR-04-06, University of Oregon, Department of Computer Science. Nov.
  29. SUN, J., CHOI, B. K., WEBER, P., AND KIECKHAFER, R. 2007. Dimple: Dynamic membership protocol for epidemic protocols. In IEEE Broadnets 2007. IEEE, Raleigh, NC, USA.
  30. TANARAKSIRITAVORN, S. AND MISHRA, S. 2004. Evaluation of gossip to build scalable and reliable multicast protocols. Performance Evaluation 58(2-3):189-214. https://doi.org/10.1016/j.peva.2004.07.014
  31. VOULGARIS, S., GAVIDIA, D., AND STEEN, M. 2005. Cyclon: Inexpensive membership management for unstructured p2p overlays. Journal of Network and Systems Management 13, 197-217. https://doi.org/10.1007/s10922-005-4441-x
  32. VOULGARIS, S., GAVIDIA, D., AND VAN STEEN, M. 2005. Cyclon: Inexpensive membership management for unstructured p2p overlays. Journal of Network and Systems Management 13(2):197-217. https://doi.org/10.1007/s10922-005-4441-x
  33. VOULGARIS, S. AND VAN STEEN, M. 2003. An epidemic protocol for managing routing tables in very large peer-to-peer networks. In Proceedings of the 14th IFIP/IEEE Workshop on Distributed Systems: Operations and Management (DSOM '03). Number 2867 in LNCS. Springer, Heidelberg, Germany.
  34. VOULGARIS, S. AND VAN STEEN, M. 2004. Epidemic-style management of semantic overlays for content-based searching. Technical Report IR-CS-011, Vrije Universiteit Amsterdam. Nov.