A Performance Model for Statistical Multiplexing of correlated ATM Traffic Superpositions

Models for ATM statistical multiplexing must involve bursty (i.e. several cell arrivals are possible at a time instant) and correlated input processes. Correlation is important, since ATM networks will carry VBR video sources, the cell streams of which are typically correlated. In addition, the superposition of individual sources (e.g. in an ATM multiplexer), which are renewal (and non-Poisson), shows correlations. This paper extends recent results on queues with deterministic service times (greater or equal to the time-unit) and Semi-Markovian input processes (SMPs) to the SMP[x]/V/l/s queue. The SMP generates arrival instants, and the independent process X of i.i.d. random variables “modulates” the batch size. The solution provides the probability functions of the number of cells in the system and of the waiting time of a test-cell, the loss probability of a test-cell and the conditional cell loss probability. A possible extension allowing one batch input stream and one single cell stream is outlined; it can serve as a model for Connection Admission Control (CAC) in ATM.

[1]  Ward Whitt,et al.  Approximating a Point Process by a Renewal Process, I: Two Basic Methods , 1982, Oper. Res..

[2]  Chris Blondia,et al.  Statistical Multiplexing of VBR Sources: A Matrix-Analytic Approach , 1992, Perform. Evaluation.

[3]  Masayuki Murata,et al.  Analysis of a Discrete-Time Single-Server Queue with Bursty Inputs for Traffic Control in ATM Networks , 1990, IEEE J. Sel. Areas Commun..

[4]  Manfred Kramer,et al.  Computational Methods for Markov Chains Occurring in Queueing Theory , 1987, MMB.

[5]  Christoph Herrmann Analysis of the discrete-time SMP/D/1/s finite buffer queue with applications in ATM , 1993, IEEE INFOCOM '93 The Conference on Computer Communications, Proceedings.

[6]  Jeffrey J. Hunter,et al.  Mathematical techniques of applied probability , 1985 .

[7]  Erhan Çinlar,et al.  Introduction to stochastic processes , 1974 .

[8]  John Cosmas,et al.  Characterization of Video Codecs as Autoregressive Moving Average Processes and Related Queueing System Performance , 1991, IEEE J. Sel. Areas Commun..

[9]  H. Ahmadi,et al.  Analysis of a discrete-time G/sup (X)//D/1-S queueing system with applications in packet-switching systems , 1988, IEEE INFOCOM '88,Seventh Annual Joint Conference of the IEEE Computer and Communcations Societies. Networks: Evolution or Revolution?.

[10]  C. Herrmann Correlation effect on per-stream QOS parameters of ATM traffic superpositions relevant to connection admission control , 1993, Proceedings of ICC '93 - IEEE International Conference on Communications.

[11]  WhittWard Approximating a Point Process by a Renewal Process, I , 1982 .

[12]  Winfried K. Grassmann,et al.  Regenerative Analysis and Steady State Distributions for Markov Chains , 1985, Oper. Res..

[13]  M. Neuts,et al.  A single-server queue with server vacations and a class of non-renewal arrival processes , 1990, Advances in Applied Probability.

[14]  Annie Gravey,et al.  On the Geo/D/1/and Geo/D/1/n Queues , 1990, Perform. Evaluation.

[15]  Jeffrey J. Hunter Discrete Time Models : Techniques and Applications , 1983 .