Study on a New Model for Network Traffic Matrix Estimation

The traffic matrix (TM) is essential in network planning and traffic engineering tasks. Lots of models and methods are proposed to estimate the network overall traffic matrix from link measurements. However because of the limits of the link measurements, the estimation on overall traffic matrix from link measurements based on these prior model assumptions do not perform well for large-scale networks. It has been proved the probability model can reconstruct the traffic matrix from limited link measurements with small bias. The probability model-based estimation is also extended to large-scale networks. We compare the probability model with the classical gravity model using real data of the Abilene network. It demonstrates the probability model is applicable in real networks. Finally we propose a model that combines the probability model and the gravity model. It is proved the performance of TM estimation based on this model is better than that based on two sole models separately.