An Accurate Approach for Traffic Matrix Estimation in Large-Scale Backbone Networks

2016 ◽  
Author(s):  
Jingli Yang ◽  
Xue Huang ◽  
Shouda Jiang
Keyword(s):  
Large Scale ◽  
Matrix Estimation ◽  
Backbone Networks ◽  
Traffic Matrix ◽  
Traffic Matrix Estimation

scholarly journals Probability-model based network traffic matrix estimation

2014 ◽  
Vol 11 (1) ◽  
pp. 309-320
Author(s):  
Hui Tian ◽  
Yingpeng Sang ◽  
Hong Shen ◽  
Chunyue Zhou
Keyword(s):  
Large Scale ◽  
Probability Model ◽  
Estimation Methods ◽  
Matrix Estimation ◽  
Large Scale Network ◽  
Traffic Matrix ◽  
Minimization Methods ◽  
Traffic Matrix Estimation ◽  
Scale Network ◽  
Mean Square Errors

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.

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