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European Journal of Operational Research
Volume 167, Issue 1, 16 November 2005, Pages 144-162
 
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doi:10.1016/j.ejor.2003.10.055    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2004 Elsevier B.V. All rights reserved.

Stochastics and Statistics

Ergodicity and analysis of the process describing the system state in polling systems with two queues

Rosa E. LilloCorresponding Author Contact Information, E-mail The Corresponding Author

Dpto. de Estadística, Universidad Carlos III de Madrid, Calle Madrid, 126, Getafe, 28903, Madrid, Spain

Received 17 June 2003; 
accepted 14 October 2003. 
Available online 9 June 2004.

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Abstract

Consider a polling system of two queues served by a single server that visits the queues in cyclic order. The polling discipline in each queue is of exhaustive-type, and zero-switchover times are considered. We assume that the arrival times in each queue form a Poisson process and that the service times form sequences of independent and identically distributed random variables, except for the service distribution of the first customer who is served at each polling instant (the time in which the server moves from one queue to the other one). The sufficient and necessary conditions for the ergodicity of such polling system are established as well as the stationary distribution for the continuous-time process describing the state of the system. The proofs rely on the combination of three embedded processes that were previously used in the literature. An important result is that ρ=1 can imply ergodicity in one specific case, where ρ is the typical traffic intensity for polling systems, and ρ<1 is the classical non-saturation condition.

Author Keywords: Queueing; Polling system; Exhaustive discipline; Ergodicity condition; Embedded processes

Article Outline

1. Introduction
2. Notation and preliminary results
3. Ergodicity conditions
4. Stationary distribution for the embedded process at service completion epochs
5. Stationary distribution for the embedded process at polling times
6. Limiting distribution for the continuous-time process
Acknowledgements
Appendix A
References


 
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