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Performance models of token ring local area networks

Berry, Robert and Chandy, K. Mani (1983) Performance models of token ring local area networks. In: Proceedings of the 1983 ACM SIGMETRICS conference on Measurement and modeling of computer systems. Association for Computing Machinery , New York, NY, pp. 266-274. ISBN 0-89791-112-1.

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This paper presents a simple heuristic analytic algorithm for predicting the “response times” of messages in asymmetric token ring local area networks. A description of the token ring and the model is presented in section 2 the algorithm is described in section 3 and the empirical results in section 4. The analytic results were compared against a detailed simulation model and the results are extremely close over a wide range of models. Local area networks (or LANS) offer a very attractive solution to the problem of connecting a large number of devices distributed over a small geographic area. They are an inexpensive readily expandable and highly flexible communications media. They are the backbone of the automated office - a significant component of the office of the future. This importance of LANS in the future of applied computer science has resulted in a tremendous burst of interest in the study of their behaviour. There are already many different LAN architectures proposed and studied in the literature [Tropper 81] [Tannenbaum 81] [Babic 78] [Metcalfe 76] [Clark 78] One LAN architecture is significant for several reasons. This architecture is the token ring [Carsten 77]. It has attracted interest because of its simplicity fairness and efficiency. The interest it has generated has resulted in the proposal of several different versions. This paper concentrates on one of these versions - the single token token ring protocol as described in [Bux 81]. This particular version is attractive because of its overall simplicity and reliability. This paper presents an algorithm for predicting response times in a token ring with the single token protocol.

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Additional Information:© 1983 ACM. This work was supported by a grant from IBM. We would like to thank Fred May of IBM Austin, for his advice and support. We also want to thank Bill McCallum, Rick Gimarc, Charlie Sauer and Jim Markov of IBM for their help.
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ID Code:92259
Deposited By: Tony Diaz
Deposited On:22 Jan 2019 23:06
Last Modified:16 Nov 2021 03:48

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