Open Access
December 2010 Fluid limits of many-server queues with reneging
Weining Kang, Kavita Ramanan
Ann. Appl. Probab. 20(6): 2204-2260 (December 2010). DOI: 10.1214/10-AAP683

Abstract

This work considers a many-server queueing system in which impatient customers with i.i.d., generally distributed service times and i.i.d., generally distributed patience times enter service in the order of arrival and abandon the queue if the time before possible entry into service exceeds the patience time. The dynamics of the system is represented in terms of a pair of measure-valued processes, one that keeps track of the waiting times of the customers in queue and the other that keeps track of the amounts of time each customer being served has been in service. Under mild assumptions, essentially only requiring that the service and reneging distributions have densities, as both the arrival rate and the number of servers go to infinity, a law of large numbers (or fluid) limit is established for this pair of processes. The limit is shown to be the unique solution of a coupled pair of deterministic integral equations that admits an explicit representation. In addition, a fluid limit for the virtual waiting time process is also established. This paper extends previous work by Kaspi and Ramanan, which analyzed the model in the absence of reneging. A strong motivation for understanding performance in the presence of reneging arises from models of call centers.

Citation

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Weining Kang. Kavita Ramanan. "Fluid limits of many-server queues with reneging." Ann. Appl. Probab. 20 (6) 2204 - 2260, December 2010. https://doi.org/10.1214/10-AAP683

Information

Published: December 2010
First available in Project Euclid: 19 October 2010

zbMATH: 1208.60094
MathSciNet: MR2759733
Digital Object Identifier: 10.1214/10-AAP683

Subjects:
Primary: 60F17 , 60K25 , 90B22
Secondary: 35D99 , 60H99

Keywords: abandonment , call centers , fluid limits , GI/G/N queue , Many-server queues , Measure-valued processes , reneging , Strong law of large numbers

Rights: Copyright © 2010 Institute of Mathematical Statistics

Vol.20 • No. 6 • December 2010
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