DocumentCode :
1554172
Title :
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
Link To Document :
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