Probability-Model based network traffic matrix estimation

Hui Tian, Yingpeng Sang, Hong Shen, Chunyue Zhou

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)309-320
Number of pages12
JournalComputer Science and Information Systems
Issue number1
Publication statusPublished - Jan 2014
Externally publishedYes


  • Probability model
  • Traffic matrix estimation


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