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Hyper-Erlang Based Model for Network Traffic Approximation

  • Junfeng Wang
  • Hongxia Zhou
  • Fanjiang Xu
  • Lei Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3758)

Abstract

The long-tailed distribution characterizes many properties of Internet traffic. The property is often modeled by Lognormal distribution, Weibull or Pareto distribution theoretically. However, it hinders us in traffic analysis and evaluation studies directly from these models due to their complex representations and theoretical properties. This paper proposes a Hyper-Erlang Model (Mixed Erlang distribution) for such long-tailed network traffic approximation. It fits network traffic with long-tailed characteristic into a mixed Erlang distribution directly to facilitate our further analysis. Compared with the well-known hyperexponential based method, the mixed Erlang model is more accurate in fitting the tail behavior and also computationally efficient.

Keywords

Cumulative Distribution Function Expectation Maximization Expectation Maximization Algorithm Tail Behavior Erlang Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Junfeng Wang
    • 1
  • Hongxia Zhou
    • 2
  • Fanjiang Xu
    • 1
  • Lei Li
    • 1
  1. 1.National Key Laboratory of Integrated Information System Technology, Institute of SoftwareChinese Academy of SciencesBeijingP.R. China
  2. 2.Chongqing Communication InstituteChongqingP.R. China

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