A data streaming algorithm for estimating entropies of OD flows

Haiquan Zhao, Oliver Spatscheck, Ashwin Lall, Jia Wang, Mitsunori Ogihara, Jun Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Scopus citations

Abstract

Entropy has recently gained considerable significance as an important metric for network measurement. Previous research has shown its utility in clustering traffic and detecting traffic anomalies. While measuring the entropy of the traffic observed at a single point has already been studied, an interesting open problem is to measure the entropy of the traffic between every origin-destination pair. In this paper, we propose the first solution to this challenging problem. Our sketch builds upon and extends the Lp sketch of Indyk with significant additional innovations. We present calculations showing that our data streaming algorithm is feasible for high link speeds using commodity CPU/memory at a reasonable cost. Our algorithm is shown to be very accurate in practice via simulations, using traffic traces collected at a tier-1 ISP backbone link.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM SIGCOMM Internet Measurement Conference, IMC
Pages279-290
Number of pages12
DOIs
StatePublished - 2007
Externally publishedYes
EventIMC'07: 2007 7th ACM SIGCOMM Internet Measurement Conference - San Diego, CA, United States
Duration: Oct 24 2007Oct 26 2007

Other

OtherIMC'07: 2007 7th ACM SIGCOMM Internet Measurement Conference
CountryUnited States
CitySan Diego, CA
Period10/24/0710/26/07

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Keywords

  • Data streaming
  • Entropy estimation
  • Network measurement
  • Stable distributions
  • Traffic matrix

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Zhao, H., Spatscheck, O., Lall, A., Wang, J., Ogihara, M., & Xu, J. (2007). A data streaming algorithm for estimating entropies of OD flows. In Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC (pp. 279-290) https://doi.org/10.1145/1298306.1298345