An accurate approach for traffic matrix estimation in large-scale backbone networks

Traffic matrix is a vital performance parameter for network management and optimization, thus it is in great need to achieve the traffic matrix accurately. Network tomography is a commonly adopted framework to estimate traffic matrix based on link loads in real networks. Since the model of network tomography always behaves the ill-posed characteristic, which means the traffic matrix estimation under network tomography framework is still a major challenge. To address this problem, a novel approach named ATME is presented. ATME can reduce the reconstruction errors of traffic matrix by using the criteria of TM's sparsity on each time slot. Besides, a prediction method based on grey predictive model is used to update the approximate value of the negative entries achieved by orthogonal match pursuit algorithm. Experimental results demonstrate that ATME is adaptive for initial value of sparsity, and can also obtain a higher accuracy on traffic matrix estimation.