Statistical multiplexing with loss priorities in rate-based congestion control of high-speed networks

Statistical multiplexing with loss priorities is a central element in ATM-based B-ISDN. Cell priorities arise from the marking schemes employed by the access regulators to identify excess cells, which are dropped during periods of congestion, Also, in real time applications, such as hierarchically coded voice and video, cells are assigned priorities which correspond to their importance to service quality, so that when congestion occurs only the least important are dropped. The authors present a stochastic fluid model of statistical multiplexing with loss priorities. Each Markov modulated fluid source generates streams of different priorities. The burstiness of each stream and the correlation between the priority streams are captured in the mode. The loss priority is implemented by selectively discarding cells of certain priority classes when the buffer content exceeds a corresponding threshold. To handle high dimensional source models, the authors develop an algebraic theory for the efficient computation of the spectrum of the statistical multiplexing system, which generalizes previous results for on-off sources. It is shown that to obtain the solution of the statistical multiplexing problem with J priority classes, J different 1-class problems need to be solved, together with a system of linear equations which describe the behavior of the stationary distribution at the thresholds. The numerical results demonstrate the manner in which i) the threshold level controls the tradeoff between delay of higher priority cells and the loss probability of lower priority cells, and ii) the buffer size controls the loss probability of higher priority cells. >

[1]  D. Mitra Stochastic theory of a fluid model of producers and consumers coupled by a buffer , 1988, Advances in Applied Probability.

[2]  J. W. Roberts,et al.  Performance evaluation and design of multiservice networks , 1992 .

[3]  Gunnar Karlsson,et al.  Performance models of statistical multiplexing in packet video communications , 1988, IEEE Trans. Commun..

[4]  A. E. Eckberg,et al.  AN APPROACH TO CONTROLLING CONGESTION IN ATM NETWORKS , 1990 .

[5]  Hans Kröner,et al.  Priority Management in ATM Switching Nodes , 1991, IEEE J. Sel. Areas Commun..

[6]  Thomas E. Stern,et al.  Analysis of separable Markov-modulated rate models for information-handling systems , 1991, Advances in Applied Probability.

[7]  San-Qi Li,et al.  Study of information loss in packet voice systems , 1989, IEEE Trans. Commun..

[8]  Lars Dittmann,et al.  Statistical multiplexing of identical bursty sources in an ATM network , 1988, IEEE Global Telecommunications Conference and Exhibition. Communications for the Information Age.

[9]  Donald F. Towsley,et al.  Approximation Techniques for Computing Packet Loss in Finite-Buffered Voice Multiplexers , 1991, IEEE J. Sel. Areas Commun..

[10]  Martin Vetterli,et al.  Joint source/channel coding of statistically multiplexed real-time services on packet networks , 1993, TNET.

[11]  Hamid Ahmadi,et al.  Equivalent Capacity and Its Application to Bandwidth Allocation in High-Speed Networks , 1991, IEEE J. Sel. Areas Commun..

[12]  Debasis Mitra,et al.  Effective bandwidth of general Markovian traffic sources and admission control of high speed networks , 1993, IEEE INFOCOM '93 The Conference on Computer Communications, Proceedings.

[13]  San-qi Li,et al.  Congestion control for packet voice by selective packet discarding , 1990, IEEE Trans. Commun..

[14]  Debasis Mitra,et al.  Stochastic fluid models in the analysis of access regulation in high speed networks , 1991, IEEE Global Telecommunications Conference GLOBECOM '91: Countdown to the New Millennium. Conference Record.

[15]  D. Mitra,et al.  Stochastic theory of a data-handling system with multiple sources , 1982, The Bell System Technical Journal.

[16]  G. Birkhoff,et al.  A survey of modern algebra , 1942 .

[17]  Sayeed Ghani,et al.  Data Performance in Burst Switching When the Voice Silence Periods Have a Hyperexponential Distribution , 1987, IEEE Trans. Commun..

[18]  J. Keilson Markov Chain Models--Rarity And Exponentiality , 1979 .

[19]  Jean-Yves Le Boudec,et al.  An Efficient Solution Method for Markov Models of ATM Links with Loss Priorities , 1991, IEEE J. Sel. Areas Commun..

[20]  Garrett Birkhoff,et al.  A survey of modern algebra , 1942 .

[21]  Richard J. Gibbens,et al.  Effective bandwidths for the multi-type UAS channel , 1991, Queueing Syst. Theory Appl..

[22]  Raj Jain,et al.  Myths about Congestion Management in High Speed Networks , 1992, INDC.