Multicast topology inference from measured end-to-end loss

  • Authors:
  • N. G. Duffield;J. Horowitz;F. Lo Presti;D. Towsley

  • Affiliations:
  • AT&T Labs-Research, Florham Park, NJ;-;-;-

  • Venue:
  • IEEE Transactions on Information Theory
  • Year:
  • 2006

Quantified Score

Hi-index 754.84

Visualization

Abstract

The use of multicast inference on end-to-end measurement has been proposed as a means to infer network internal characteristics such as packet link loss rate and delay. We propose three types of algorithm that use loss measurements to infer the underlying multicast topology: (i) a grouping estimator that exploits the monotonicity of loss rates with increasing path length; (ii) a maximum-likelihood estimator (MLE); and (iii) a Bayesian estimator. We establish their consistency, compare their complexity and accuracy, and analyze the modes of failure and their asymptotic probabilities