Adaptive Statistical Multiplexing for Broadband Communication

  • Timothy X. Brown
Part of the The International Series in Engineering and Computer Science book series (SECS, volume 557)


Statistical multiplexing requires a decision function to classify which source combinations can be multiplexed through a given packet network node while meeting quality of service guarantees. This chapter shows there are no practical fixed statistical multiplexing decision functions that carry reasonable loads and rarely violate quality of service requirements under all distributions of source combinations. It reviews adaptive alternatives and presents statistical-classification-based decision functions that show promise across many distributions including difficult-to-analyze ethernet data, distributions with cross-source correlations, and traffic with mis-specified parameters.


Asynchronous Transfer Mode Quality of Service Admission Control Statistical Multiplexing Adaptive Methods 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [Bis95]
    Bishop, C., Neural Networks for Pattern Recognition, Oxford U. Press, Oxford, 1992. 482p.Google Scholar
  2. [Bro95]
    Brown, T.X., “Classifying loss rates with small samples,” in Proc. of IWANNT, Erlbaum, Hillsdale, NJ, 1995. pp. 153–161Google Scholar
  3. [Bro97]
    Brown, T.X., “Adaptive access control applied to ethernet data,” Advances in Neural Information Processing Systems, 9, MIT Press, 1997. pp. 932–8.Google Scholar
  4. [Bro99a]
    Brown, T. X, Tong, H., Singh, S., “Optimizing admission control while ensuring quality of service in multimedia networks via reinforcement learning,” in Advances in Neural Information Processing Systems, 11, MIT Press, 1999, pp. 982–8.Google Scholar
  5. [Bro99b]
    Brown, T. X., “Classifying loss rates in broadband networks,” in 1NFOCOMM’ 99, New York, April v. 1, pp. 361–70, 1999.Google Scholar
  6. [Che92]
    Chen, X., Leslie, I.M., “Neural adaptive congestion control for broadband ATM,” IEE Proc.-I, v. 139, n. 3, pp. 233–40, 1992.Google Scholar
  7. [Cho94]
    Choudhury, G.L., Lucantoni, D.M., Whitt, W., “On the effectiveness of admission control in ATM networks,” in the 14th International Teletraffic Congress in France, June 6–10, 1994. pp. 411–20.Google Scholar
  8. [Dud73]
    Duda, R.O., Hart, P.E., Pattern Classification and Scene Analysis, Wiley & Sons, New York, 1973.Google Scholar
  9. [Elw93]
    Elwalid, A. I., Mitra, D. “Effective bandwidth of general Markovian traffic sources and admission control of high-speed networks,” IEEE/ACM Trans. on Networking, v. 1, n. 3, June 1993.Google Scholar
  10. [Est94]
    Estrella, A.D., Jurado, A., Sandoval, F., “New training pattern selection method for ATM call admission neural control,” Elec. Let., v. 30, n. 7, pp. 577–9, Mar. 1994.Google Scholar
  11. [Err96]
    Erramilli, A., Narayan, O., Willinger, W., “Experimental queueing analysis with long-range dependent packet traffic,” IEEE/ACM T. on Networking, v. 4, n. 2, pp. 209–3, April 1996.Google Scholar
  12. [Gal95]
    Galmes, S., et al., “Effectiveness of the ATM forum source traffic description,” in Local and Metropolitan Communication Systems, v. 3. ed. Hasegawa, T., et al. Chapman and Hall, 1995. pp. 93–107.Google Scholar
  13. [Gar94]
    Garrett, M.W., Willinger, W., “Analysis, modeling and generation of self-similar VBR video traffic,” in Proc. of ACM SIGCOMM, 1996. pp.269–80.Google Scholar
  14. [Gro96]
    Grossglauser, M., Bolot, J-C., “On the relevance of long-range dependence in network traffic,” in Proc. of ACM SIGCOMM, 1994. pp. 15–24.Google Scholar
  15. [Gue91]
    Guerin, R., Ahmadi, H., Naghshineh, M., “Equivalent capacity and its application to bandwidth allocation in high-speed networks,” IEEE JSAC, v. 9, n. 7, pp. 968–81, 1991.Google Scholar
  16. [Gue99]
    Guerin, R., Peris, V., “Quality of service in packet networks: basic mechanisms and directions,” Computer Networks and ISDN Systems, v.31, n.3, 1999. pp. 169–89Google Scholar
  17. [Hey96]
    Heyman, D.P, Lakshman, T.V., “Source models for VBR broadcast-video traffic,” IEEE/ACM T. on Networking, v. 4, n. 6, pp. 40–8, 1996.Google Scholar
  18. [Hir90]
    Hiramatsu, A., “ATM communications network control by neural networks,” IEEE T. on Neural Networks, v. 1, n. 1, pp. 122–30, 1990.Google Scholar
  19. [Hir95]
    Hiramatsu, A., “Training techniques for neural network applications in ATM,” IEEE Comm. Mag., October, pp. 58–67, 1995.Google Scholar
  20. [Jam92]
    Jamin, S., et al., “An admission control algorithm for predictive real-time service,” Third Int. Workshop Proc. of Network and Operating Systems Support for Digital Audio and Video, 1992. pp. 349–56.Google Scholar
  21. [Kaw95]
    Kawamura, Y., Saito, H., “VP bandwidth management with dynamic connection admission control in ATM networks,” in Local and Metropolitan Communication Systems, vol. 3. ed. Hasegawa, T., et al. Chapman and Hall, London, 1995. pp. 233–52.Google Scholar
  22. [Kni99]
    Knightly, E.W., Shroff, N.B., “Admission Control for Statistical QoS: Theory and Practice,” IEEE Network, March/April 1999, pp. 20–9.Google Scholar
  23. [Kri95]
    Krishnan, K.R., “The Hurst parameter of non-Markovian on-off traffic sources,” Bellcore Technical Memorandum, Feb., 1995.Google Scholar
  24. [Lau93]
    Lau, W.C., Li, S.Q., “Traffic analysis in large-scale high-speed integrated networks: validation of nodal decomposition approach” Proc. of lNFOCOMM, v. 3, 1993. pp. 1320–29.Google Scholar
  25. [Lee96]
    Lee, D.C., “Worst-case fraction of CBR teletraffic unpunctual due to statistical multiplexing,” IEEE/ACM Tran. on Networking, v. 4, n. 1, Feb. 1996. pp. 98–105.Google Scholar
  26. [Lel93]
    Leland, W.E., et al., “On the self-similar nature of ethernet traffic,” in Proc. of ACM SlGCOMM 1993. pp. 183–3, also in IEEE/ACM T. on Networking, v. 2, n. 1, pp. 1–15, 1994.Google Scholar
  27. [Lev97]
    Levin, B., Ericsson Project Report, to appear.Google Scholar
  28. [Mit88]
    Mitra, D., “Stochastic theory of a fluid model of producers and consumers coupled by a buffer,” Adv. Appl. Prob., v. 20, pp.646–76, 1988.zbMATHGoogle Scholar
  29. [Mit98]
    Mitra, D., Reiman, M.I., Wang, J., “Robust dynamic admission control for unified cell and call QoS in statistical multiplexers,” IEEE JSAC, v. 16, n. 5, pp. 692–707, 1998.Google Scholar
  30. [Nev93]
    Neves, J.E., et al., “ATM call control by neural networks,” in Proc. Inter. Workshop on Applications of Neural Networks to Telecommunication,” Erlbaum, Hillsdale, NJ, pp. 210–7, 1993.Google Scholar
  31. [Nor93]
    Nordstrom, E., “A hybrid admission control scheme for broadband ATM traffic,” in Proc. IWANNT, Erlbaum, pp. 77–84, 1993.Google Scholar
  32. [Nor94]
    Norros, I., “A storage model with self-similar input,” Queueing Systems, v. 16, pp. 387–96, 1994CrossRefzbMATHMathSciNetGoogle Scholar
  33. [Pax94]
    Paxson, V., Floyd, S., “Wide-area traffic: The failure of Poisson modeling,” in Proc. of ACM SIGCOMM, 1994. pp. 257–68.Google Scholar
  34. [Ton98]
    Tong, H., Brown, T. X., “Estimating Loss Rates in an Integrated Services Network by Neural Networks,” in Proc. of Global Telecommunications Conference (GLOBECOM 98), v. 1, pp. 19–24, 1998.Google Scholar
  35. [Ton99]
    Tong, H., Brown, T.X., “Adaptive call admission control under quality of service constraints: a reinforcement learning solution,” to appear in IEEE JSAC, Feb. 2000.Google Scholar
  36. [Tra92]
    Tran-Gia, P., Gropp, O., “Performance of a neural net used as admission controller in ATM systems,” Proc. GLOBECOM 92, Orlando, FL, pp. 1303–9.Google Scholar
  37. [Wil95]
    Willinger, W., Taqqu, M.S., Sherman, R., Wilson, D.V., “Self-similarity through high-variability: statistical analysis of ethernet LAN traffic at the source level,” Bellcore Internal Memo, Feb. 7, 1995. Also in IEEE/ACM T. on Networking, v. 5, n. 1, pp. 71–86, 1997.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Timothy X. Brown
    • 1
  1. 1.Dept. of Electrical and Computer EngineeringUniversity of ColoradoBoulder

Personalised recommendations