Multicast topology inference from measured end-to-end loss

Author

Duffield, N.G. ; Horowitz, Joseph ; Presti, Francesco Lo ; Towsley, Don

Author_Institution

AT&T Labs-Research, Florham Park, NJ, USA

Volume

48

Issue

1

fYear

2002

fDate

1/1/2002 12:00:00 AM

Firstpage

26

Lastpage

45

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

Keywords

Bayes methods; delays; inference mechanisms; loss measurement; maximum likelihood estimation; multicast communication; network topology; packet switching; probability; telecommunication links; trees (mathematics); Bayesian estimator; MLE; asymptotic probabilities; binary loss trees; complexity; failure modes; grouping estimator; logical topology; maximum-likelihood estimator; measured end-to-end loss; multicast topology inference; network internal characteristics; packet delay; packet link loss rate; path length; Bayesian methods; Inference algorithms; Internet; Length measurement; Loss measurement; Maximum likelihood estimation; Multicast algorithms; Network topology; Performance evaluation; Probes;

fLanguage

English

Journal_Title

Information Theory, IEEE Transactions on

Publisher

ieee

ISSN

0018-9448

Type

jour

DOI

10.1109/18.971737

Filename

971737