Probability-Model based Network Traffic Matrix Estimation

Hui Tian1, Yingpeng Sang2, Hong Shen3, 4 and Chunyue Zhou1

  1. School of Electronics and Information Engineering
    Beijing Jiaotong University, China
  2. School of Computer Science
    Beijing Jiaotong University, China
  3. School of Information Science and Technology
    Sun Yat-sen University, China
  4. School of Computer Science
    University of Adelaide, Australia


Traffic matrix is of great help in many network applications. However, it is very difficult to estimate the traffic matrix for a large-scale network. This is because the estimation problem from limited link measurements is highly underconstrained. 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.

Key words

traffic matrix estimation, probability model, NRMSE

Digital Object Identifier (DOI)

Publication information

Volume 11, Issue 1 (January 2014)
Year of Publication: 2014
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Tian, H., Sang, Y., Shen, H., Zhou, C.: Probability-Model based Network Traffic Matrix Estimation. Computer Science and Information Systems, Vol. 11, No. 1, 309–320. (2014),