An In-Depth, Analytical Study of Sampling Techniques For Self-Similar Internet Traffic

He, Guanghui; Hou, Jennifer C.

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https://hdl.handle.net/2142/10919 Copy

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Title

An In-Depth, Analytical Study of Sampling Techniques For Self-Similar Internet Traffic

Author(s)

• He, Guanghui
• Hou, Jennifer C.

Issue Date

2004-10

Keyword(s)

• internet traffic
• networking

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.

Type of Resource

text

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http://hdl.handle.net/2142/10919

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