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

Abstract

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.