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An improved topology inference algorithm based on end-to-end measurements

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Network topology inference, one of the studies of the network tomography, is the proposition of the network link-level performance inference. MLE and grouping methods recently have been proposed as means to infer network logical topology. The time spent on MLE increased sharply with the size of network, which may restrict the technique to be used in practice. The grouping method with less computation may lead to great errors because of the use of fixed threshold. In order to improve the disadvantages of the grouping method, this paper proposes an improved algorithm based on th grouping method, which dynamically adapts the threshold according to the estimation of link loss rate. The simulation results show that the improved algorithm has greater performance.

Original languageEnglish
Title of host publicationProceedings - 2010 1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010
Pages661-665
Number of pages5
DOIs
StatePublished - 2010
Event1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010 - Harbin, China
Duration: 17 Sep 201019 Sep 2010

Publication series

NameProceedings - 2010 1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010

Conference

Conference1st International Conference on Pervasive Computing, Signal Processing and Applications, PCSPA 2010
Country/TerritoryChina
CityHarbin
Period17/09/1019/09/10

Keywords

  • End-to-end measurements
  • Loss rate
  • Network tomography inference
  • Network topology

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