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End-to-End Network Traffic Reconstruction Via Network Tomography Based on Compressive Sensing

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Abstract

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.

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Acknowledgments

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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  1. College of Information Science and Engineering, 4th Floor, Computing Centre, Northeastern University, No. 11, Lane 3, Wen Hua Road, Shenyang, 110819, People’s Republic of China

    Laisen Nie, Dingde Jiang & Lei Guo

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  1. Laisen Nie
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  2. Dingde Jiang
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  3. Lei Guo
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Correspondence to Dingde Jiang.

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Nie, L., Jiang, D. & Guo, L. End-to-End Network Traffic Reconstruction Via Network Tomography Based on Compressive Sensing. J Netw Syst Manage 23, 709–730 (2015). https://doi.org/10.1007/s10922-014-9314-8

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  • DOI: https://doi.org/10.1007/s10922-014-9314-8

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