Stochastic Sampling for Internet Traffic Measurement

Title: Stochastic Sampling for Internet Traffic Measurement

Authors: Tilman Wolf

Abstract: The increasing complexity of the Internet demands continued improvements to measurement techniques and data analysis methods to aid our understanding of network operation. The availability of accurate measurement data is necessary in many areas ranging from attack detection, novel pricing schemes, buffer dimensioning and switch design to general network management. In this paper, we develop a theory for accurate and unbiased Internet traffic measurement using the tools of Poisson random sampling. We show how this approach helps in storing, managing, and aggregating data from different sources with independent clocks and sampling rates. We present results that show that stochastic sampling maintains important information about network measurements that would be lost when using conventional uniform sampling.

Published: Tilman Wolf, Yan Cai, Patrick A. Kelly, and Weibo Gong, "Stochastic sampling for internet traffic measurement," in Proc. of 10th IEEE Global Internet Symposium, Anchorage, AK, May 2007.

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BibTeX:
@inproceedings{Wolf:SSI07,
  author    = {Wolf, Tilman and Cai, Yan and Kelly, Patrick A. and Gong, Weibo},
  title     = {Stochastic Sampling for Internet Traffic Measurement},
  booktitle = {Proc. of 10th IEEE Global Internet Symposium},
  year      = 2007,
  pages     = {},
  address   = {Anchorage, AK},
  month     = may
}

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