Traffic matrix estimation: existing techniques and new directions

By Alberto Medina, Nina Taft, Kave Salamatian, Supratik Bhattacharyya, Christophe Diot. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2002

581 citations recorded by the source corpus.

Very few techniques have been proposed for estimating traffic matrices in the context of Internet traffic. Our work on POP-to-POP traffic matrices (TM) makes two contributions. The primary contribution is the outcome of a detailed comparative evaluation of the three existing techniques. We evaluate these methods with respect to the estimation errors yielded, sensitivity to prior information required and sensitivity to the statistical assumptions they make. We study the impact of characteristics such as path length and the amount of link sharing on the estimation errors. Using actual data from a Tier-1 backbone, we assess the validity of the typical assumptions needed by the TM estimation techniques. The secondary contribution of our work is the proposal of a new direction for TM estimation based on using choice models to model POP fanouts. These models allow us to overcome some of the problems of existing methods because they can incorporate additional data and information about POPs and they enable us to make a fundamentally different kind of modeling assumption. We validate this approach by illustrating that our modeling assumption matches actual Internet data well. Using two initial simple models we provide a proof of concept showing that the incorporation of knowledge of POP features (such as total incoming bytes, number of customers, etc.) can reduce estimation errors. Our proposed approach can be used in conjunction with existing or future methods in that it can be used to generate good priors that serve as inputs to statistical inference techniques.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/828ead67f334517d7db11dd9f3a496485a29675c

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Citations in the corpus

An information-theoretic approach to traffic matrix estimation (2003, methodology): “While there is experience with the gravity model above and some success in its application, it is also known that it g ives results that are not as accurate as may be obtained using addi tional information [16, 28].”
Spatio-temporal compressive sensing and internet traffic matrices (2009, background): “The thrust of much of this research has been on measurement [10,28] and inference [9,17, 25,27,31–34] of TMs, and more recently on topics such as anomaly detection [13, 14, 21, 29, 30].”
Providing public intradomain traffic matrices to the research community (2006, unknown): “Providing Public Intradomain Traf.c Matrices to the Research Community Steve Uhlig1, Bruno Quoitin Jean Lepropre2 , Simon Balon2 Computing Science and Engineering Research Unit in Networking Department University of Liege, Belgium University of Louvain-la-Neuve, Belgium {lepropre,balon}@run.monte”
Fast accurate computation of large-scale IP traffic matrices from link loads (2003, methodology): “The paper concluded [7] that none of the prior methods for computing traffic matrices was satis f ctory for even a PoP to PoP traffic matrix on a large network, let alon e a BR to BR matrix.”
Data streaming algorithms for efficient and accurate estimation of flow size distribution (2004, background): “Finally, knowing the flow distribution of each link may help other network measurement applications such as traffic matrix estimation [7, 8, 9, 10].”
OpenTM: Traffic Matrix Estimator for OpenFlow Networks (2010, methodology): “Indirect methods are sensitive to the statistical assumptions made in their models and are shown to have large errors [6].”
Structural analysis of network traffic flows (2004, background): “However, a wide range of important problems faced by network researchers today require modeling and analysis of traffic on all links simultaneously, including traffic engineering, traffic matrix estimation [18, 19, 27, 33, 34], anomaly detection [1, 6], attack detection [32], traffic forecasting and”