A Markovian approach for modeling packet traffic with long-range dependence

  • Authors:
  • A. T. Andersen;B. F. Nielsen

  • Affiliations:
  • Dept. of Math. Modeling, Tech. Univ., Lyngby;-

  • Venue:
  • IEEE Journal on Selected Areas in Communications
  • Year:
  • 2006

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Abstract

We present a simple Markovian framework for modeling packet traffic with variability over several time scales. We present a fitting procedure for matching second-order properties of counts to that of a second-order self-similar process. Our models essentially consist of superpositions of two-state Markov modulated Poisson processes (MMPPs). We illustrate that a superposition of four two-state MMPPs suffices to model second-order self-similar behavior over several time scales. Our modeling approach allows us to fit to additional descriptors while maintaining the second-order behavior of the counting process. We use this to match interarrival time correlations