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On the Use of Principle Component Analysis for the Hurst Parameter Estimation of Long-Range Dependent Network Traffic

  • Melike Erol
  • Tayfun Akgul
  • Sema Oktug
  • Suleyman Baykut
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4263)

Abstract

Long-range dependency and self-similarity are the major characteristics of the Internet traffic. The degree of self-similarity is measured by the Hurst parameter (H). Various methods have been proposed to estimate H. One of the recent methods is an eigen domain estimator which is based on Principle Component Analysis (PCA); a popular signal processing tool. The PCA-based Method (PCAbM) uses the progression of the eigenvalues which are extracted from the autocorrelation matrix. For a self-similar process, this progression obeys a power-law relationship from which H can be estimated. In this paper, we compare PCAbM with some of the well-known estimation methods, namely; periodogram-based, wavelet-based estimation methods and show that PCAbM is reliable only when the process is long-range dependent (LRD), i.e., H is greater than 0.5. We also apply PCAbM and the other estimators to real network traces.

Keywords

Principle Component Analysis Fractional Brownian Motion Hurst Parameter Regression Plot Autocorrelation Matrix 
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 2006

Authors and Affiliations

  • Melike Erol
    • 1
  • Tayfun Akgul
    • 2
  • Sema Oktug
    • 1
  • Suleyman Baykut
    • 2
  1. 1.Department of Computer EngineeringIstanbul Technical UniversityIstanbulTurkey
  2. 2.Department of Electronics and Communications EngineeringIstanbul Technical UniversityIstanbulTurkey

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