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

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3 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıComputer and Information Sciences - ISCIS 2006
Ana bilgisayar yayını alt yazısı21th International Symposium, Proceedings
YayınlayanSpringer Verlag
Sayfalar464-473
Sayfa sayısı10
ISBN (Basılı)3540472428, 9783540472421
DOI'lar
Yayın durumuYayınlandı - 2006
EtkinlikISCIS 2006: 21th International Symposium on Computer and Information Sciences - Istanbul, Turkey
Süre: 1 Kas 20063 Kas 2006

Yayın serisi

AdıLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Hacim4263 LNCS
ISSN (Basılı)0302-9743
ISSN (Elektronik)1611-3349

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???event.eventtypes.event.conference???ISCIS 2006: 21th International Symposium on Computer and Information Sciences
Ülke/BölgeTurkey
ŞehirIstanbul
Periyot1/11/063/11/06

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