1st International ICST Conference on Performance Evaluation Methodologies and Tools

Research Article

Mean value analysis for polling systems in heavy traffic

  • @INPROCEEDINGS{10.1145/1190095.1190155,
        author={R.D.  van der Mei and E.M.M.  Winands},
        title={Mean value analysis for polling systems in heavy traffic},
        proceedings={1st International ICST Conference on Performance Evaluation Methodologies and Tools},
        publisher={ACM},
        proceedings_a={VALUETOOLS},
        year={2012},
        month={4},
        keywords={polling systems mean value analysis heavy traffic delay visit time.},
        doi={10.1145/1190095.1190155}
    }
    
  • R.D. van der Mei
    E.M.M. Winands
    Year: 2012
    Mean value analysis for polling systems in heavy traffic
    VALUETOOLS
    ACM
    DOI: 10.1145/1190095.1190155
R.D. van der Mei1,2,*, E.M.M. Winands3,4,*
  • 1: Dept. of Mathematics, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands.
  • 2: Centre for Mathematics and Computer Science, 1098 SJ Amsterdam, The Netherlands
  • 3: Dept. of Mathematics and Computer Science, Dept. of Technology Management, Technische Universiteit Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The
  • 4: Netherlands.
*Contact email: mei@cwi.nl, e.m.m.winands@tue.nl

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 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-from expressions for the expected asymptotic delay; the expressions were derived earlier in [31], 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.