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Experiments with Improved Approximate Mean Value Analysis Algorithms

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Computer Performance Evaluation (TOOLS 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1469))

Abstract

Approximate Mean Value Analysis (MVA) is a popular technique for analyzing queueing networks because of the efficiency and accuracy that it affords. In this paper, we present a new software package, called the Improved Approximate Mean Value Analysis Library (IAMVAL), which can be easily integrated into existing commercial and research queueing network analysis packages. The IAMVAL packages includes two new approximate MVA algorithms, the Queue Line (QL) algorithm and the Fraction Line (FL) algorithm, for analyzing multiple class separable queueing networks. The QL algorithm is always more accurate than, and yet has approximately the same computational efficiency as, the Bard-Schweitzer Proportional Estimation (PE) algorithm, which is currently the most widely used approximate MVA algorithm. The FL algorithm has the same computational cost and, in noncongested separable queueing networks where queue lengths are quite small, yields more accurate solutions than both the QL and PE algorithms.

This research was supported by the Natural Science and Engineering Research Council of Canada (NSERC), and by Communications and Information Technology Ontario (CITO).

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Authors and Affiliations

  1. Department of Computer Science, University of Toronto, Toronto, Ontario, Canada, M5S 3G4

    Hai Wang & Kenneth C. Sevcik

Authors
  1. Hai Wang
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  2. Kenneth C. Sevcik
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Editors and Affiliations

  1. Departament de Ciencies Matematiques i Informàtica, Universitat de les Illes Balears, Careterra de Valldemossa km 7.6, E-07071, Palma (Balears), Spain

    Ramon Puigjaner , Nunzio N. Savino  & Bartomeu Serra ,  & 

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© 1998 Springer-Verlag Berlin Heidelberg

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Wang, H., Sevcik, K.C. (1998). Experiments with Improved Approximate Mean Value Analysis Algorithms. In: Puigjaner, R., Savino, N.N., Serra, B. (eds) Computer Performance Evaluation. TOOLS 1998. Lecture Notes in Computer Science, vol 1469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-68061-6_23

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  • DOI: https://doi.org/10.1007/3-540-68061-6_23

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64949-6

  • Online ISBN: 978-3-540-68061-1

  • eBook Packages: Springer Book Archive

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