research-article

Multicast topology inference from measured end-to-end loss

Authors:

N. G. Duffield,

J. Horowitz,

F. Lo Presti,

D. TowsleyAuthors Info & Claims

IEEE Transactions on Information Theory, Volume 48, Issue 1

Pages 26 - 45

https://doi.org/10.1109/18.971737

Published: 01 September 2006 Publication History

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

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IEEE Transactions on Information Theory Volume 48, Issue 1

January 2002

346 pages

ISSN:0018-9448

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IEEE Press

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Published: 01 September 2006

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Li YDeng K(2022)Simultaneous Topology and Loss Tomography via a Modified Theme Dictionary ModelIEEE Transactions on Signal Processing10.1109/TSP.2022.319180770(4239-4251)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TSP.2022.3191807
Gray CMosig CBush RPelsser CRoughan MSchmidt TWahlisch M(2020)BGP Beacons, Network Tomography, and Bayesian Computation to Locate Route Flap DampingProceedings of the ACM Internet Measurement Conference10.1145/3419394.3423624(492-505)Online publication date: 27-Oct-2020
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