An Adaptive Method for Dynamic Audience Size Estimation in Multicast

  • Maziar Nekovee
  • Andrea Soppera
  • Trevor Burbridge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2816)


We develop an end-to-end protocol for real-time estimation of the size of dynamic multicast groups. Unlike previously proposed methods our approach alleviates feedback implosion in a dynamic setting, and is scalable to large groups. The protocol is based on probabilistic polling combined with adaptive feedback control, and the use of a time-dependent Wiener filter to enhance estimation accuracy. We examine the performance of our protocol through simulations for multicast groups with up to 10,000 members, and different scenarios of group membership dynamics. Our simulation studies show that the method is capable of tracking, in a scalable manner, the size of dynamic multicast groups with high accuracy in the face of large dynamic variations.


Group Size Packet Loss Multicast Group IEEE INFOCOM Probabilistic Polling 
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  1. 1.
    Schulzrinne, H., Casner, S., Fredrick, R., Jacobson, V.: RTP: a transport protocol for real-time applications. RFC 1889, Network Working Group (January 1996)Google Scholar
  2. 2.
    Rosenberg, J., Schulzrinne, H.: Timer reconsideration for enhanced RTP scalability. In: Proc. of IEEE INFOCOM 1998, San Francisco, USA, pp. 488–496 (1998)Google Scholar
  3. 3.
    Floyd, S., Jacobson, V., McCanne, S., Liu, C., Zhang, L.: A reliable multicast framework for light-weight session and application level framing. In: Proc. of ACM SIGCOMM 1995, New York, USA, pp. 342–356 (1995)Google Scholar
  4. 4.
    Handley, M., Bormann, C., Adamson, B.: NACK-oriented reliable multicast (NORM) Protocl Bulding Blocks. INTERNET-DRAFT (September 2003),
  5. 5.
    See, E.G., Henderson, T., Bhatti, S.N.: Protocol independent multicast pricing. In: 10th International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSS-DV 2000), The University of North Carolina, USA (June 2000)Google Scholar
  6. 6.
    Almeroth, K., Ammar, M.: Collecting and modeling of join/leave behavior of multicast group members in MBone. In: Proc. of HPDC 1996, Syracuse, NY, USA, August 1996, pp. 209–216 (1996)Google Scholar
  7. 7.
    Bolot, J.-C., Turletti, T., Wakeman, I.: Scalable feedback control for multicast video distribution in the Internet. In: Proc. of ACM SIGCOMM 1994, London, UK, September 1994, pp. 58–67 (1994)Google Scholar
  8. 8.
    Friedman, T., Towsley, D.: Multicast session membership size estimation. In: Proc. of IEEE INFOCOM 1999, New York, NY USA, March 1999, vol. 2, pp. 965–972 (1999)Google Scholar
  9. 9.
    Nonnenmacher, J.: Reliable multicast to large groups. Ph.D. thesis, EPFL, Laussane, Switzerland (July 1998); Nonnenmacher, J., Biersack, E. W.: Scalable feedback for large groups. IEEE/ACM Transactions on Networking, 375–386 (June 1999) Google Scholar
  10. 10.
    Liu, C., Nonnenmacher, J.: Broadcast audience estimation. In: Proc. of IEEE INFOCOM 2000, Tel Aviv, Israel, vol. 2, pp. 952–960 (March 2000)Google Scholar
  11. 11.
    Alouf, S., Altman, E., Nain, P.: Optimal on-line estimation of the size of a dynamic multicast group. In: Proc. of IEEE INFOCOM 2002, New York, NY, USA, vol. 2, pp. 952–960 (March 2002)Google Scholar
  12. 12.
    Lee, P.M.: Bayesian Statistics: An Introduction. Arnold Publishing, London (1997)zbMATHGoogle Scholar
  13. 13.
    Grimmet, G.R., Stirzaker, D.R.: Probability and Random Processes. Clarendon Press, Oxford (1992)Google Scholar
  14. 14.
    Abramovitz, M., Stegun, I.: Handbook of Mathematical Functions. Dover Publications, New York (1972)Google Scholar
  15. 15.
    Hiaswatsch, S.F., Matz, G., Kirchauer, H., Kozek, W.: Time-frequency formulation, design, and implementation of time-varying optimal filters for signal estimation. IEEE Transactions on Signal Processing 48, 1417–1432 (references therein)Google Scholar
  16. 16.
    Jacobs, R.J., Walker, M.D., Jeffry, R.I.M.: – automated multimedia content delivery. BT Technology Journal 20, 75–83 Google Scholar
  17. 17.
    We thank the anonymous referee for drawing our attention to this scenario Google Scholar
  18. 18.
    Yanjik, M., Moon, S., Kurose, J., Towsley, D.: Measurement and modeling of the temporal dependence in packet loss. In: Proceedings of IEEE INFOCOM 1999, Los Alamitos, California (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Maziar Nekovee
    • 1
  • Andrea Soppera
    • 1
  • Trevor Burbridge
    • 1
  1. 1.BT ExactMartlesham, SuffolkUK

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