Files in this item



application/pdfAn In-Depth, An ... milar Internet Traffic.pdf (3MB)
(no description provided)PDF


Title:An In-Depth, Analytical Study of Sampling Techniques For Self-Similar Internet Traffic
Author(s):He, Guanghui; Hou, Jennifer C.
Subject(s):internet traffic
Abstract:Internet traffic sampling techniques are very important to understand the traffic characteristics of the Internet, and have received increasing attention. In spite of all the research efforts, none has taken into account the self-similarity of Internet traffic in analyzing and devising sampling strategies. In this paper, we perform an in-depth, analytical study of three sampling techniques for self-similar Internet traffic, namely static systematic sampling, stratified random sampling and simple random sampling. We show that while all three sampling techniques can accurately capture the Hurst parameter (second order statistics) of Internet traffic, they fail to capture the mean (first order statistics) faithfully, due to the bursty nature of Internet traffic. We also show that static systematic sampling renders the smallest variation of sampling results in different instances of sampling (i.e., it gives sampling results of high fidelity). Based on an important observation, we then devise a new variation of static systematic sampling, called biased systematic sampling (BSS), that gives much more accurate estimates of the mean, while keeping the sampling overhead low. Both the analysis on the three sampling techniques and the evaluation of BSS are performed on synthetic and real Internet traffic traces. The performance evaluation shows that BSS gives a performance improvement of 42% and 23% (in terms of efficiency) as compared to static systematic and simple random sampling.
Issue Date:2004-10
Genre:Technical Report
Other Identifier(s):UIUCDCS-R-2004-2484
Rights Information:You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS:2009-04-17

This item appears in the following Collection(s)

Item Statistics