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Performance Evaluation
Volume 65, Issues 6-7, June 2008, Pages 400-416
Innovative Performance Evaluation Methodologies and Tools: Selected Papers from ValueTools 2006
 
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doi:10.1016/j.peva.2007.12.002    
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Copyright © 2007 Elsevier Ltd All rights reserved.

Heavy traffic analysis of polling models by mean value analysis

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R.D. van der Meia, b, Corresponding Author Contact Information, E-mail The Corresponding Author and E.M.M. Winandsc, d, E-mail The Corresponding Author

aDepartment of Mathematics, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands

bCentre for Mathematics and Computer Science (CWI), 1098 SJ Amsterdam, The Netherlands

cDepartment of Mathematics and Computer Science, Technische Universiteit Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands

dDepartment of Technology Management, Technische Universiteit Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The Netherlands


Received 12 October 2007; 
accepted 7 December 2007. 
Available online 14 December 2007.

Abstract

In this paper we present a new approach to derive heavy-traffic asymptotics for polling models. We consider the classical cyclic polling model with exhaustive or gated service at each queue, and with general service-time and switch-over time distributions, and study its behavior when the load tends to one. For this model, we explore the recently proposed mean value analysis (MVA), which takes a new view on the dynamics of the system, and use this view to provide an alternative way to derive closed-form expressions for the expected asymptotic delay; the expressions were derived earlier in [R.D. van der Mei, H. Levy, Expected delay in polling systems in heavy traffic, Adv. Appl. Probab. 30 (1998) 586–602], but in a different way. Moreover, the MVA-based approach enables us to derive closed-form expressions for the heavy-traffic limits of the covariances between the successive visit periods, which are key performance metrics in many application areas. These results, which have not been obtained before, reveal a number of insensitivity properties of the covariances with respect to the system parameters under heavy-traffic assumptions, and moreover, lead to simple approximations for the covariances between the successive visit times for stable systems. Numerical examples demonstrate that the approximations are accurate when the load is close enough to one.

Keywords: Polling systems; Mean value analysis; Heavy traffic; Delay; Visit time

Article Outline

1. Introduction
2. Model description and notation
3. Performance measures
4. Exhaustive service
4.1. Mean value analysis
4.2. Performance measures
4.3. Approximations
5. Gated service
5.1. Mean value analysis
5.2. Performance measures
6. Specific application area for correlations between visit times
7. Model extensions
Acknowledgements
References
Vitae



Corresponding Author Contact InformationCorresponding author at: Department of Mathematics, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands.

Performance Evaluation
Volume 65, Issues 6-7, June 2008, Pages 400-416
Innovative Performance Evaluation Methodologies and Tools: Selected Papers from ValueTools 2006
 
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