Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-08T04:22:43.595Z Has data issue: false hasContentIssue false

Some Pitfalls of Black Box Queue Inference: The Case of State-Dependent Server Queues

Published online by Cambridge University Press:  27 July 2009

Sheldon M. Ross
Affiliation:
Department of Industrial Engineering and Operations Research, University of California, Berkeley, California 94720
J. George Shanthikumar
Affiliation:
Walter A. Haas School of Business, University of California, Berkeley, California 94720
Xiang Zhang
Affiliation:
Department of Industrial Engineering and Operations Research, University of California, Berkeley, California 94720

Abstract

In several queueing systems the service rate of a server is affected by the work load present in the system. For example, a teller at a bank or a checker at a check-out counter in a supermarket may change the service rate depending on the number of customers present in the system. But the service rate as a function of the number in the system can rarely be measured. Consequently, in a typical model of such a system it is assumed that the service rate is constant. Hence, such systems with a single stage are often modeled by GI/GI/c queueing systems with mutually independent arrival and service processes. Then the observed service times are used to find a sample distribution that will represent the distribution of the assumed i.i.d. service times. The purpose of this paper is to explore the effect of this black box queue inference (BBQI) in its ability to predict the performance of the actual system. In this regard, we have shown that when the arrival process is Poisson, if the servers react favorably [unfavorably] to higher work loads (i.e., if the server increases [decreases] the service rate as the number of customers in the system increases) then the BBQI predictions will be pessimistic [optimistic]. This result can be used to identify the server's attitude toward higher work load.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1993

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

1.Barlow, R.E. & Proschan, F. (1981). Statistical theory of reliability and life testing: Probability models. Silver Spring, MD: To Begin With.Google Scholar
2.Buzacott, J.A. (1974). The effect of queue discipline on the capacity of queues with service time depending on waiting time. INFOR 12: 174185.Google Scholar
3.Harris, T.E. (1977). A correlation inequality for Markov processes in partially ordered state space. Annals of Probability 5: 451454.Google Scholar
4.Marshall, A.W. & Olkin, I. (1979). Inequalities: Theory of majorization and its applications. Orlando, FL: Academic Press.Google Scholar
5.Ross, S.M. (1983). Stochastic processes. New York: Wiley.Google Scholar
6.Shaked, M. & Shanthikumar, J.G. (1990). Parametric stochastic convexity and concavity of stochastic processes. Annals of the Institute of Statistical Mathematics 42: 509531.Google Scholar