A Compressive Sensing-Based Approach to End-to-End Network Traffic Reconstruction

作者：Jiang, Dingde^*; Wang, Wenjuan; Shi, Lei; Song, Houbing

来源：IEEE Transactions on Network Science and Engineering, 2020, 7(1): 507-519.

DOI：10.1109/TNSE.2018.2877597

• Traffic flow
• compressive sensing
• measurement matrix
• sparse matrix
• traffic reconstruction

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.

• 出版日期2020-1
• 单位电子科技大学; 东北大学