Paper Info
Title
Local and Cumulative Analysis of Self-similar Traffic Traces
Abstract
Internet traffic shows variability in all time scales, which in turn shows statistical self-similarity. This selfsimilar behaviour has significant implications for QoS since it increments the total delay and packet loss rate. Therefore, we need to test for the degree of selfsimilarity and use this information for control purposes. For achieving the above-mentioned, the use of traces consisting of several thousands of points and hours of measurement are used. However, there are not enough studies about the number of points required to get an accurate estimation of the Hurst exponent. In this article, we study the local and cumulative behaviour of many real and synthetic self-similar traces. This is done for trying to infer the number of points required for Hurst parameter estimation and for checking dependence of Hurst exponents. We show that local analysis presents self-similarity, and the Hurst exponent tends to be stable in the cumulative case.
Year
DOI
Venue
2006
10.1109/CONIELECOMP.2006.37
CONIELECOMP
Keywords
Field
DocType
local analysis,internet traffic,long-memory estimators,self-similar traffic traces,long-range dependence,accurate estimation,control purpose,statistical self-similarity,cumulative lrd analysis,local lrd analysis,cumulative case,hurst parameter estimation,self-similarity,cumulative analysis,cumulative behaviour,selfsimilar behaviour,hurst exponent,long memory,cumulant,hurst parameter,self similarity
Hurst parameter estimation,Computer science,Hurst exponent,Packet loss rate,Quality of service,Local analysis,Statistics,Self-similarity,Internet traffic
Conference
ISBN
Citations 
PageRank 
0-7695-2505-9
0
0.34
References 
Authors
5
2
Name
Order
Citations
PageRank
Ramirez Pacheco Julio Cesar110.69
Torres Roman Deni210.69