article

Network tomography on general topologies

Authors:

Francesco Lo Presti,

Don TowsleyAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 30, Issue 1

Pages 21 - 30

https://doi.org/10.1145/511399.511338

Published: 01 June 2002 Publication History

Abstract

In this paper we consider the problem of inferring link-level loss rates from end-to-end multicast measurements taken from a collection of trees. We give conditions under which loss rates are identifiable on a specified set of links. Two algorithms are presented to perform the link-level inferences for those links on which losses can be identified. One, the minimum variance weighted average (MVWA) algorithm treats the trees separately and then averages the results. The second, based on expectation-maximization (EM) merges all of the measurements into one computation. Simulations show that EM is slightly more accurate than MVWA, most likely due to its more efficient use of the measurements. We also describe extensions to the inference of link-level delay, inference from end-to-end unicast measurements, and inference when some measurements are missing.

References

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Information & Contributors

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Published In

cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 30, Issue 1

Measurement and modeling of computer systems

June 2002

286 pages

ISSN:0163-5999

DOI:10.1145/511399

Issue’s Table of Contents

SIGMETRICS '02: Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
June 2002
299 pages
ISBN:1581135319
DOI:10.1145/511334
General Chair:
Richard Muntz
UCLA
,
Program Chairs:
Margaret Martonosi
Princeton University
,
Edmundo de Souza e Silva
UFRJ, Brazil

Copyright © 2002 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2002

Published in SIGMETRICS Volume 30, Issue 1

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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
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