Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-23T20:57:33.226Z Has data issue: false hasContentIssue false
  • English
  • Français

Heuristics for Finite-Buffer Queues

Published online by Cambridge University Press:  27 July 2009

Henk Tijms
Henk Tijms
Affiliation:
Department of EconometricsVrije Universiteit, De Boelelaan 1105 1081 HV Amsterdam, The Netherlands
Get access Rights & Permissions [Opens in a new window]

Abstract

The approximation of the loss probability in finite-buffer queues is a practical problem of considerable interest. A new heuristic is given. This heuristic is based on the equilibrium distribution of the corresponding infinite-buffer queue. Numerical results show an excellent performance of the heuristic.

Type
Articles
Information
Probability in the Engineering and Informational Sciences , Volume 6 , Issue 3 , July 1992 , pp. 277 - 285
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

Schweitzer, P.J. & Konheim, A. (1978). Buffer overflow calculations using an infinite-capacity model. Stochastic Processes and Their Applications 6: 267–276.CrossRefGoogle Scholar
Tak´cs, L. (1962). Introduction to the theory of queues. New York: Oxford University Press.Google Scholar
Tijms, H.C. (1986). Stochastic modeling and analysis: A computational approach. New York:Wiley.Google Scholar
Tijms, H. C. & Van, de Coevering M.C. (1991). A simple numerical approach for infinite-state Markov chains. Probability in the Engineering and Informational Sciences 5: 285–295.CrossRefGoogle Scholar
Tijms, H.C. & Van, Ommeren J.C.W. (1989). Asymptotic analysis for buffer behavior in communication systems. Probability in the Engineering and Informational Sciences 3: 1–12.CrossRefGoogle Scholar
Wolff, R.W. (1982). Poisson arrivals see time averages. Operations Research 30: 223–231.CrossRefGoogle Scholar