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.
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Erol, M., Akgul, T., Oktug, S., Baykut, S. (2006). On the Use of Principle Component Analysis for the Hurst Parameter Estimation of Long-Range Dependent Network Traffic. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds) Computer and Information Sciences – ISCIS 2006. ISCIS 2006. Lecture Notes in Computer Science, vol 4263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11902140_50
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DOI: https://doi.org/10.1007/11902140_50
Publisher Name: Springer, Berlin, Heidelberg
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