Efficient solution of multiple server queues with application to the modeling of ATM concentrators

This paper introduces a method for obtaining the steady state probabilities in G/sup [X]//D/C/K type queues. The solution method is more efficient than other known techniques in terms of both time and space requirements. The method is an extension of the MBH technique used to solve single server queues, It is shown that this method allows one to perform a parametric study quickly and efficiently by building on already existing results. The solution technique is used to evaluate the performance of concentrators used in ATM networks where low speed lines are connected to higher speed ones, e.g., 150 Mb/s lines to a 620 Mb/s or 1.2 Gb/s line. The concentrator is equipped with a finite capacity buffer, and is modeled as a synchronous multiple server queue with finite buffer. The concentrator is fed by a discrete batch Markov arrival process (D-BMAP) which is capable of capturing the traffic characteristics of a broad range of applications. The paper derives the probability of cell loss introduced by the concentrator buffering, and considers the effect of the buffer size as well as the line speeds on this measure.

[1]  Richard D. Gitlin,et al.  A framework for a national broadband (ATM/B-ISDN) network , 1990, IEEE International Conference on Communications, Including Supercomm Technical Sessions.

[2]  A. Yavuz Oruç,et al.  High performance concentrators and superconcentrators using multiplexing schemes , 1994, IEEE Trans. Commun..

[3]  Marcel F. Neuts THE c - SERVER QUEUE WITH CONSTANT SERVICE TIMES AND A VERSATILE , 1982 .

[4]  John G. Kemeny,et al.  Finite Markov chains , 1960 .

[5]  Anthony S. Acampora,et al.  The Knockout Switch: A Simple, Modular Architecture for High-Performance Packet Switching , 1987, IEEE J. Sel. Areas Commun..

[6]  David M. Lucantoni,et al.  New results for the single server queue with a batch Markovian arrival process , 1991 .

[7]  Victor S. Frost,et al.  A new solution technique for discrete queueing analysis of ATM system , 1991, IEEE Global Telecommunications Conference GLOBECOM '91: Countdown to the New Millennium. Conference Record.

[8]  John G. Kemeny,et al.  Finite Markov Chains. , 1960 .

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

[10]  P. Jacobs,et al.  Finite Markov chain models skip-free in one direction , 1984 .

[11]  Jorge García-Vidal,et al.  A Discrete Time Queueing Model to Study the Cell Delay Variation in an ATM Network , 1994, Perform. Evaluation.

[12]  S. C. Tang,et al.  The queue length distribution for multiserver discrete time queues with batch markovian arrivals , 1992 .

[13]  M. Neuts A Versatile Markovian Point Process , 1979 .

[14]  Chris Blondia,et al.  Performance analysis of statistical multiplexing of VBR sources , 1992, [Proceedings] IEEE INFOCOM '92: The Conference on Computer Communications.

[15]  Philip C. Kelly,et al.  Modeling and Analysis of Computer-Communication Networks , 1980 .

[16]  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..

[17]  Ji Zhang,et al.  Spectral Decomposition Approach for Transient Analysis of Multi-Server Discrete-Time Queues , 1994, Perform. Evaluation.

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