期刊
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
卷 7, 期 1, 页码 507-519出版社
IEEE COMPUTER SOC
DOI: 10.1109/TNSE.2018.2877597
关键词
Traffic flow; compressive sensing; measurement matrix; sparse matrix; traffic reconstruction
资金
- National Natural Science Foundation of China [61571104]
- Sichuan Science and Technology Program [2018JY0539]
- Key Projects of the Sichuan Provincial Education Department [18ZA0219]
- Fundamental Research Funds for the Central Universities [ZYGX2017KYQD170, N150402003]
- General Project of Scientific Research of the Education Department of Liaoning Province [L20150174]
Estimation of end-to-end network traffic plays an important role in traffic engineering and network planning. The direct measurement of a network's traffic matrix consumes large amounts of network resources and is thus impractical in most cases. How to accurately construct traffic matrix remains a great challenge. This paper studies end-to-end network traffic reconstruction in large-scale networks. Applying compressive sensing theory, we propose a novel reconstruction method for end-to-end traffic flows. First, the direct measurement of partial Origin-Destination (OD) flows is determined by random measurement matrix, providing partial measurements. Then, we use the K-SVD approach to obtain a sparse matrix. Combined with compressive sensing, this partially known OD flow matrix can be used to recover the entire end-to-end network traffic matrix. Simulation results show that the proposed method can reconstruct end-to-end network traffic with a high degree of accuracy. Moreover, in comparison with previous methods, our approach exhibits a significant performance improvement.
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