@inproceedings{8622eb81b63949ab94bd4624fa46a073,
title = "On the use of principle component analysis for the hurst parameter estimation of long-range dependent network traffic",
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.",
author = "Melike Erol and Tayfun Akgul and Sema Oktug and Suleyman Baykut",
year = "2006",
doi = "10.1007/11902140_50",
language = "English",
isbn = "3540472428",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "464--473",
booktitle = "Computer and Information Sciences - ISCIS 2006",
address = "Germany",
note = "ISCIS 2006: 21th International Symposium on Computer and Information Sciences ; Conference date: 01-11-2006 Through 03-11-2006",
}