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Effective Bandwidths for Stationary Sources

Published online by Cambridge University Press:  27 July 2009

Costas Courcoubetis  and
Richard Weber
Costas Courcoubetis
Affiliation:
Department of Computer Science, University of Crete, PO Box 1470, Heraklion, Greece, 71110
Richard Weber
Affiliation:
Statistical Laboratory, University of Cambridge, 16 Mill Lane, Cambridge CB2 1SB, United Kingdom
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Abstract

At a buffered switch in an ATM (asynchronous transfer mode) network it is important to know what combinations of different types of traffic can be carried simultaneously without risking more than a very small probability of overflowing the buffer. We show that a simple and serviceable measure of effective bandwidths may be computed for stationary traffic sources. For large buffers the effective bandwidth of a source is a function only of its mean rate, index of dispersion, and the size of the buffer.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 9 , Issue 2 , April 1995 , pp. 285 - 296
Copyright
Copyright © Cambridge University Press 1995

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References

1.Bean, N.G. (1993). Statistical multiplexing in broadband communications networks. Ph.D. thesis, University of Cambridge, Cambridge, UK.Google Scholar
2.Bucklew, J.A. (1990). Large deviation techniques in decision, simulation and estimation. New York: John Wiley.Google Scholar
3.Chatfield, C. (1975). The analysis of time series: Theory and practice. London: Chapman and Hall.CrossRefGoogle Scholar
4.Courcoubetis, C., Fouskas, G. & Weber, R.R. (1994). On the performance of an effective bandwidths formula. In Labetoulle, J. & Roberts, J.W. (eds.). The fundamental role of teletraffic in the evolution of telecommunications networks. Proceedings of the Nth International Teletraffic Congress. Amsterdam: Elsevier, pp. 201–212.Google Scholar
5.Courcoubetis, C., Kesidis, G., Ridder, A., Walrand, J. & Weber, R.R. (1995). Admission control and routing in ATM networks using inferences from measured buffer occupancy. IEEE Transactions on Communications 43: 1778–1784.Google Scholar
6.Courcoubetis, C. & Walrand, J. (1991). Note on the effective bandwidth of ATM traffic at a buffer. Technical Report TR-036, Institute of Computer Science, Hellas Crete.Google Scholar
7.De Veciana, G., Olivier, C. & Walrand, J. (1993). Large deviations of birth death Markov fluids. Probability in the Engineering and Informational Sciences 7: 237–255.Google Scholar
8.De Veciana, G. & Walrand, J. (1993). Effective bandwidths: Call admission, traffic policing & filtering for ATM networks. Technical Report UNB/ERL M93/47, University of California, Berkeley.Google Scholar
9.Dembo, A. & Zeitouni, O. (1993). Large deviations techniques and applications. Boston: Jones and Bartlett.Google Scholar
10.Gibbens, R. & Hunt, P. (1991). Effective bandwidths for the multi-type UAS channel. Queueing Systems 9: 17–28.CrossRefGoogle Scholar
11.Kelly, F.P. (1991). Effective bandwidths at multi-class queues. Queueing Systems 9: 5–16.Google Scholar
12.Kesidis, G., Walrand, J. & Chang, C.S. (1993). Effective bandwidths for multiclass Markov fluids and other ATM sources. IEEE Transactions on Networking 1: 424–428.CrossRefGoogle Scholar