Open Access
February 2011 Law of large numbers limits for many-server queues
Haya Kaspi, Kavita Ramanan
Ann. Appl. Probab. 21(1): 33-114 (February 2011). DOI: 10.1214/09-AAP662

Abstract

This work considers a many-server queueing system in which customers with independent and identically distributed service times, chosen from a general distribution, enter service in the order of arrival. The dynamics of the system are represented in terms of a process that describes the total number of customers in the system, as well as a measure-valued process that keeps track of the ages of customers in service. Under mild assumptions on the service time distribution, as the number of servers goes to infinity, a law of large numbers (or fluid) limit is established for this pair of processes. The limit is characterized as the unique solution to a coupled pair of integral equations which admits a fairly explicit representation. As a corollary, the fluid limits of several other functionals of interest, such as the waiting time, are also obtained. Furthermore, when the arrival process is time-homogeneous, the measure-valued component of the fluid limit is shown to converge to its equilibrium. Along the way, some results of independent interest are obtained, including a continuous mapping result and a maximality property of the fluid limit. A motivation for studying these systems is that they arise as models of computer data systems and call centers.

Citation

Download Citation

Haya Kaspi. Kavita Ramanan. "Law of large numbers limits for many-server queues." Ann. Appl. Probab. 21 (1) 33 - 114, February 2011. https://doi.org/10.1214/09-AAP662

Information

Published: February 2011
First available in Project Euclid: 17 December 2010

zbMATH: 1208.60095
MathSciNet: MR2759196
Digital Object Identifier: 10.1214/09-AAP662

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

Keywords: call centers , fluid limits , GI∕G∕N queue , mean-field limits , Measure-valued processes , Multi-server queues , Strong law of large numbers

Rights: Copyright © 2011 Institute of Mathematical Statistics

Vol.21 • No. 1 • February 2011
Back to Top