Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/108592
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Type: Conference paper
Title: New methods for network traffic matrix estimation based on a probability model
Author: Tian, H.
Sang, Y.
Shen, H.
Citation: Proceedings of the 17th IEEE International Conference on Networks, 2011, pp.270-274
Publisher: IEEE
Publisher Place: Online
Issue Date: 2011
ISBN: 9781457718250
Conference Name: 17th IEEE International Conference on Networks (ICON) (14 Dec 2011 - 16 Dec 2011 : Singapore, Singapore)
Statement of
Responsibility: 
Hui Tian, Yingpeng Sang and Hong Shen
Abstract: Traffic matrix is of great help in many network applications. However, it is very difficult, if not intractable, to estimate the traffic matrix for a large-scale network. This is because the estimation problem from limited link measurements is highly under-constrained. We propose a simple probability model for a large-scale practical network. The probability model is then generalized to a general model by including random traffic data. Traffic matrix estimation is then conducted under these two models by two minimization methods. It is shown that the Normalized Root Mean Square Errors of these estimates under our model assumption are very small. For a large-scale network, the traffic matrix estimation methods also perform well. The comparison of two minimization methods shown in the simulation results complies with the analysis.
Keywords: Traffic matrix estimation; probability model; NRMSE
Rights: © 2011 IEEE
DOI: 10.1109/ICON.2011.6168487
Published version: http://dx.doi.org/10.1109/icon.2011.6168487
Appears in Collections:Aurora harvest 8
Computer Science publications

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