Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-27T08:14:03.372Z Has data issue: false hasContentIssue false

Some Large Deviations Results in Markov Fluid Models

Published online by Cambridge University Press:  27 July 2009

Ad Ridder
Affiliation:
Rotterdam School of Management Erasmus University of Rotterdam, PO. Box 1738 3000 DR Rotterdam, The Netherlands
Jean Walrand
Affiliation:
ECS Department University of California at Berkeley, Berkeley, California 94720, U.S.A.

Abstract

Markov modulated fluid models are studied in this paper. When the input of the fluid model is represented by one Markov chain, two approaches are given that result in asymptotic expressions for the overflow probability. Both approaches are based on large deviations theories. The equivalence of the expressions is proved. When the input is represented by N similar Markov chains, a reduction property is derived.

Type
Articles
Copyright
Copyright © Cambridge University Press 1992

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anick, D., Mitra, D., & Sondhi, M.M. (1982). Stochastic theory of a data-handling system with multiple sources. The Bell System Technical Journal 61: 18711894.CrossRefGoogle Scholar
Cottrell, M., Fort, J.-C., & Malgouyres, G. (1983). Large deviations and rare events in the study of stochastic algorithms. IEEE Transactions on Automatic Control 9: 907920.CrossRefGoogle Scholar
Courcoubetis, C., Kesidis, G., Ridder, A., Walrand, J., & Weber, R. (1991). Admission control and routing in ATM networks using inferences from measured buffered occupancy. Memorandum UCB/ERL M91/37, Electronics Research Laboratory, College of Engineering, University of California, Berkeley, 04 1991.Google Scholar
Donsker, M.D. & Varadhan, S.R.S. (1975). Asymptotic evaluation of certain Markov process expectations for large time, part I. Communications on Pure and Applied Mathematics 28: 147.CrossRefGoogle Scholar
Ellis, R.S. (1985). Entropy, large deviations and statistical mechanics. New York: Springer.Google Scholar
Parekh, S. & Walrand, J. (1989). A quick simulation method for excessive backlog in network of queues. IEEE Transactions on Automatic Control 34: 5466.CrossRefGoogle Scholar
Ridder, A. (1991). Applications of large deviations to obtain variance reduction in simulating Markov fluid models. Working Paper 98, Rotterdam School of Management, Erasmus University, Rotterdam, 08 1991.Google Scholar
Rockafellar, R.T. (1970). Convex analysis. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Varadhan, S.R.S. (1984). Large deviations and applications. CBMS-NSF Regional Conference Series in Applied Mathematics, SIAM, Philadelphia.CrossRefGoogle Scholar
Ventsel, A.D. (1976). Rough limit theorems on large deviations for Markov stochastic processes, part II. Theory of Probability and Its Applications 21: 499512.CrossRefGoogle Scholar
Walrand, J. (1988). An introduction to queueing networks. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Weiss, A. (1986). A new technique of analyzing large traffic systems. Advances of Applied Probability 18: 506532.CrossRefGoogle Scholar