Practical passive lossy link inference

  • Authors:
  • Alexandros Batsakis;Tanu Malik;Andreas Terzis

  • Affiliations:
  • Department of Computer Science, Johns Hopkins University;Department of Computer Science, Johns Hopkins University;Department of Computer Science, Johns Hopkins University

  • Venue:
  • PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
  • Year:
  • 2005

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Abstract

We propose a practical technique for the identification of lossy network links. Our scheme is based on a function that computes the likelihood of each link to be lossy. This function mainly depends on the number of times a link appears in lossy paths and on the relative loss rates of these paths. Preliminary simulation results show that our solution achieves accuracy comparable to statistical methods (e.g. Bayesian) at significantly lower running time.