doiSerbia

Probability-model based network traffic matrix estimation

Tian Hui (School of Electronics and Information Engineering Beijing Jiaotong University, China)
Sang Yingpeng (School of Computer Science Beijing Jiaotong University, China)
Shen Hong (School of Information Science and Technology Sun Yat-sen University, China School of Computer Science University of Adelaide, Australia)
Zhou Chunyue (School of Electronics and Information Engineering Beijing Jiaotong University, China)

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.

Keywords: traffic matrix estimation, probability model, NRMSE