New methods for network traffic matrix estimation based on a probability model

Traffic matrix is of great help in many network applications. However, it is very difficult, if not intractable, to estimate the traffic matrix for a large-scale network. This is because the estimation problem from limited link measurements is highly under-constrained. 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.