End-to-End Network Traffic Reconstruction Via Network Tomography Based on Compressive Sensing
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Traffic matrices (TM) represent the volumes of end-to-end network traffic between each of the origin–destination pairs. Accurate estimates of TM are used by network operators to perform network management functions and traffic engineering tasks. Despite a large number of methods devoted to the problem of traffic matrix estimation, the inference of end-to-end network traffic is still a main challenge in the large-scale IP backbone network, due to an ill-posed nature of itself. In this paper, we focus on the problem of end-to-end network traffic reconstruction. Based on the network tomography method, we propose a simple method to estimate end-to-end network traffic from the aggregated data. By analyzing, in depth, the properties of the network tomography method, compressive sensing reconstruction algorithms are put forward to overcome the ill-posed nature of the network tomography model. In this case, to satisfy the technical conditions of compressive sensing, we propose a modified network tomography model. Besides, we give a further discussion that the proposed model follows the constraints of compressive sensing. Finally, we validate our method by real data from the Abilene and GÉANT backbone networks.
KeywordsTraffic matrix estimation Network measurement Convex optimization Statistical inference
This work was supported in part by the Program for New Century Excellent Talents in University (No. NCET-11-0075) and the Fundamental Research Funds for the Central Universities (Nos. N120804004, N130504003). The authors wish to thank the reviewers for their helpful comments.
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