Geodesic
Probability-Model based Network Traffic Matrix Estimation
Computer Science and Information Systems, Tome 11 (2014) no. 1.

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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 
@article{CSIS_2014_11_1_a20,
     author = {Hui Tian and Yingpeng Sang and Hong Shen and Chunyue Zhou},
     title = {Probability-Model based {Network} {Traffic} {Matrix} {Estimation}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {11},
     number = {1},
     year = {2014},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2014_11_1_a20/}
}
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AU  - Hui Tian
AU  - Yingpeng Sang
AU  - Hong Shen
AU  - Chunyue Zhou
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JO  - Computer Science and Information Systems
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VL  - 11
IS  - 1
PB  - mathdoc
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%A Yingpeng Sang
%A Hong Shen
%A Chunyue Zhou
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%N 1
%I mathdoc
%U http://geodesic.mathdoc.fr/item/CSIS_2014_11_1_a20/
%F CSIS_2014_11_1_a20
Hui Tian; Yingpeng Sang; Hong Shen; Chunyue Zhou. Probability-Model based Network Traffic Matrix Estimation. Computer Science and Information Systems, Tome 11 (2014) no. 1. http://geodesic.mathdoc.fr/item/CSIS_2014_11_1_a20/