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Employing Data Driven Random Membership Subset Algorithm for QoS-Aware Peer-to-Peer Streaming

  • Huang Yongxiang
  • Qian Depei
  • Wu Weiguo
  • Zhao Haixiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5630)

Abstract

Peer-to-peer (P2P) applications such as media broadcasting and content distribution often require that an overlay be constructed, and that some form of node selection take place over that overlay. Previous approaches to building such overlays focused mainly on high performance (leading to a rather brittle network of connections), or robustness (leading to low performance). In this paper, we present a data driven random membership (DDRM) algorithm, which tries to find a balance between the two, selecting peers for performance when needed, and at random (for robustness) if possible. The simulation experiment results show that the algorithm is not only QoS-Aware, but also ensures the scalability and good connectivity of the overlay.

Keywords

Access Rate Random Early Detection Category Node Data Chunk Playback Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Liang, J., Nahrstedt, K.: Dagstream: Locality aware and failure resilient peer-to-peer streaming. In: The 13th SPIE/ACM Multimedia Computing and Networking Conference (January 2006)Google Scholar
  2. 2.
    Padmanabhan, V.N., Wang, H.J., Chou, P.A.: Resilient peer-to-peer streaming. In: Proceeding of 11th IEEE International Conference on Network Protocols, pp. 16–27 (November 2003)Google Scholar
  3. 3.
    Zhang, X., Liu, J., Li, B., Yum, T.S.P.: Donet: A data-driven overlay network for efficient live media streaming. In: IEEE INFOCOM 2005, pp. 2102–2111 (March 2005)Google Scholar
  4. 4.
    Rejaie, R., Stafford, S.: A framework for architecting peer-to-peer receiver-driven overlays. In: NOSS-DAV 2004, pp. 42–47 (June 2004)Google Scholar
  5. 5.
    Sripanidkulchai, K., Ganjam, A., Maggs, B., Zhang, H.: The feasibility of supporting large-scale live streaming applications with dynamic application end-points. In: ACM SIGCOMM 2004, pp. 107–120 (October 2004)Google Scholar
  6. 6.
    Liang, J., Nahrstedt, K.: Randpeer: Membership management for qos sensitive peer-to-peer applications. In: Proceeding of the IEEE INFOCOM (March 2006)Google Scholar
  7. 7.
    Hei, S., Liang, C., Liang, J., Liu, Y., Ross, K.: A measurement study of a large-scale p2p iptv system. IEEE Transactions on Multimedia 9, 1672–1687 (2007)CrossRefGoogle Scholar
  8. 8.
    Yongxiang, H., Depei, Q., WeiGuo, W., Tao, L.: Non-uniform random membership management to construct overlays for transferring scalable video coding. Journal of Xi’an JiaoTong University to be publishedGoogle Scholar
  9. 9.
    Chawathe, Y., Ratnasamy, S., Breslau, L., Lanham, N., Shenker, S.: Making gnutella-like p2p systems scalable. In: Proceeding of the ACM SIGCOMM (August 2003)Google Scholar
  10. 10.
    Cooper, B.: Quickly routing searches without having to move content. In: Castro, M., van Renesse, R. (eds.) IPTPS 2005. LNCS, vol. 3640, pp. 163–172. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Loguinov, D., Kumar, A., Rai, V., Ganesh, S.: Graph-theoretic analysis of structured peer-to-peer systems: Routing distances and fault resilience. In: Proceedings of the ACM SIGCOMM, pp. 395–406 (August 2003)Google Scholar
  12. 12.
    Tsoumakos, D., Roussopoulos, N.: Adaptive probabilistic search for peer-to-peer networks. In: Proceedings of the 3rd IEEE International Conference on P2P Computing (2003)Google Scholar
  13. 13.
    Chawathe, Y., Ratnasamy, S., Breslau, L., Lanham., N.: Gia: Making gnutella-like p2p systems scalable. In: Proceedings of ACM SIGCOMM, pp. 407–418 (August 2003)Google Scholar
  14. 14.
    Zhong, M., Shen, K., Seiferas, J.: Non-uniform random membership management in peer-to-peer networks. In: Proceedings of IEEE INFORCOM, pp. 1151–1161 (March 2005)Google Scholar
  15. 15.
    Azar, Y., Broder, A.Z., Karlin, A.R., Linial, N., Phillips, S.: Biased random walks. In: Proceedings of the 24th ACM Sympo. on the Theory of Computing, pp. 1–9 (1992)Google Scholar
  16. 16.
    Cohen, B.: Incentives build robustness in bittorrent. In: The 1st Workshop on Economics of Peer-to-Peer Systems, Berkeley (June 2003)Google Scholar
  17. 17.
    Zhong, M., Shen, K., Seiferas, J.: The convergence-guaranteed random walk and its applications in peer-to-peer networks. IEEE Transanction on Computers 57, 619–633 (2008)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Saroiu, S., Gummadi, P.K., Gribble, S.D.: A measurement study of peer-to-peer file sharing systems. In: Proceedings of Multimedia Computing and Networking (2002)Google Scholar
  19. 19.
  20. 20.
    Medina, A., Lakhina, A., Matta, I., Byers, J.: Brite: An approach to universal topology generation. In: International Workshop on Modeling, Analysis and Simulation of Computer and Telecommunications Systems (2001)Google Scholar
  21. 21.
    Ke, C.H., Shieh, C.K., Hwang, W.S., Ziviani, A.: An evaluation framework for more realistic simulations of mpeg video transmission. Journal of Information Science and Engineering, 425–440 (March 2008)Google Scholar
  22. 22.
    Ganesh, A.J., Kermarrec, A., Massoulie, L.: Scamp: Peer-to-peer lightweight membership service for large-scale group communication. In: Crowcroft, J., Hofmann, M. (eds.) NGC 2001. LNCS, vol. 2233, pp. 44–55. Springer, Heidelberg (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Huang Yongxiang
    • 1
  • Qian Depei
    • 1
    • 2
  • Wu Weiguo
    • 1
  • Zhao Haixiang
    • 1
  1. 1.Computer Department of Xi’An Jiaotong UniversityXi’AnChina
  2. 2.Sina-German Software Institute of BeiHang UniversityBeiJingChina

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