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Performance Modeling and Network Management for Self-similar Traffic

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Performance Evaluation and Applications of ATM Networks

Part of the book series: The International Series in Engineering and Computer Science ((SECS,volume 557))

Abstract

Since the discovery of the self-similar nature of network traffic, researchers were able to propose new traffic models [Mayor96d, Norros94] that are better able to mimic the long-range dependence phenomenon exhibited by real network traffic. Nevertheless, since most of the existing queueing theory is based on the assumption of Markovian models, there are few analytical results dealing with an ATM queueing system driven by a self-similar process [Addie95b, Duffield95, Likhanov95, Mayor96d, Parulekar96, Ryu96a]. In this work, we give an overview of traffic models and analytical tools capable of computing tail probabilities of an ATM queueing system driven by a self-similar process. We also explain the meaning of long-range dependence and its impact on network performance and network management protocols, by revisiting Mandelbrot’s work[Mandelbrot69]. We propose a traffic characterization based on a fractional Brownian motion envelope process. By using this characterization, we show a framework derived in [Mayor96d] capable of computing bandwidth and buffer requirements in ATM networks driven by aggregate, heterogeneous, self-similar processes.

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Author information

Authors and Affiliations

  1. McKinsey & Company, Inc. Sao Paulo, Sao Paulo, Brazil, 04717-004

    Gilberto Mayor

  2. Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, California, USA, 90089-2562

    John Silvester

Authors
  1. Gilberto Mayor
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  2. John Silvester
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Editor information

Editors and Affiliations

  1. University of Bradford, UK

    Demetres Kouvatsos

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© 2002 Kluwer Academic Publishers

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Mayor, G., Silvester, J. (2002). Performance Modeling and Network Management for Self-similar Traffic. In: Kouvatsos, D. (eds) Performance Evaluation and Applications of ATM Networks. The International Series in Engineering and Computer Science, vol 557. Springer, Boston, MA. https://doi.org/10.1007/0-306-47023-3_15

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  • DOI: https://doi.org/10.1007/0-306-47023-3_15

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-7923-7851-8

  • Online ISBN: 978-0-306-47023-3

  • eBook Packages: Springer Book Archive

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