Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/34993
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Type: Conference paper
Title: An information-theoretic approach to traffic matrix estimation
Author: Zhang, Y.
Roughan, M.
Lund, C.
Donoho, D.
Citation: Proceedings of ACM SIGCOMM 2003 /A. Feldmann and M. Zitterbart,August 25-29 August, 2003: pp. 301-312
Publisher: ACM Press
Publisher Place: New York, NY USA
Issue Date: 2003
ISSN: 0146-4833
1943-5819
Conference Name: Association for Computing Machinery Special Interest Group on Data Communication conference (2003 : Karlsruhe, Germany)
Statement of
Responsibility: 
Yin Zhang, Matthew Roughan, Carsten Lund and David Donoho
Abstract: Traffic matrices are required inputs for many IP network management tasks: for instance, capacity planning, traffic engineering and network reliability analysis. However, it is difficult to measure these matrices directly, and so there has been recent interest in inferring traffic matrices from link measurements and other more easily measured data. Typically, this inference problem is ill-posed, as it involves significantly more unknowns than data. Experience in many scientific and engineering fields has shown that it is essential to approach such ill-posed problems via regularization. This paper presents a new approach to traffic matrix estimation using a regularization based on "entropy penalization". Our solution chooses the traffic matrix consistent with the measured data that is informationtheoretically closest to a model in which source/destination pairs are stochastically independent. We use fast algorithms based on modern convex optimization theory to solve for our traffic matrices. We evaluate the algorithm with real backbone traffic and routing data, and demonstrate that it is fast, accurate, robust, and flexible.
Description: Copyright 2003 ACM
DOI: 10.1145/972426.944768
Published version: http://dx.doi.org/10.1145/972426.944768
Appears in Collections:Applied Mathematics publications
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