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SET-VALUED PERFORMANCE APPROXIMATIONS FOR THE $GI/GI/K$ QUEUE GIVEN PARTIAL INFORMATION

Published online by Cambridge University Press:  25 September 2020

Yan Chen Open the ORCID record for Yan Chen [Opens in a new window]  and
Ward Whitt Open the ORCID record for Ward Whitt [Opens in a new window]
Yan Chen
Affiliation:
Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USA E-mails: yc3107@columbia.edu; ww2040@columbia.edu
Ward Whitt
Affiliation:
Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027, USA E-mails: yc3107@columbia.edu; ww2040@columbia.edu
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Abstract

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In order to understand queueing performance given only partial information about the model, we propose determining intervals of likely values of performance measures given that limited information. We illustrate this approach for the mean steady-state waiting time in the $GI/GI/K$ queue. We start by specifying the first two moments of the interarrival-time and service-time distributions, and then consider additional information about these underlying distributions, in particular, a third moment and a Laplace transform value. As a theoretical basis, we apply extremal models yielding tight upper and lower bounds on the asymptotic decay rate of the steady-state waiting-time tail probability. We illustrate by constructing the theoretically justified intervals of values for the decay rate and the associated heuristically determined interval of values for the mean waiting times. Without extra information, the extremal models involve two-point distributions, which yield a wide range for the mean. Adding constraints on the third moment and a transform value produces three-point extremal distributions, which significantly reduce the range, producing practical levels of accuracy.

Keywords

boundsextremal queuesmean waiting timemulti-server queuesperformance approximationsqueues

Information

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 36 , Issue 2 , April 2022 , pp. 378 - 400
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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