Fast accurate computation of large-scale IP traffic matrices from link loads
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Traffic matrices: balancing measurements, inference and modeling
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Robust traffic matrix estimation with imperfect information: making use of multiple data sources
SIGMETRICS '06/Performance '06 Proceedings of the joint international conference on Measurement and modeling of computer systems
LSSP: A novel local segment-shared protection for multi-domain optical mesh networks
Computer Communications
Traffic monitor deployment in IP networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Spatio-temporal compressive sensing and internet traffic matrices
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
GARCH model-based large-scale IP traffic matrix estimation
IEEE Communications Letters
Blind maximum likelihood estimation of traffic matrices under long-range dependent traffic
Computer Networks: The International Journal of Computer and Telecommunications Networking
Joint time-frequency sparse estimation of large-scale network traffic
Computer Networks: The International Journal of Computer and Telecommunications Networking
An approximation method of origin-destination flow traffic from link load counts
Computers and Electrical Engineering
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
A fast lightweight approach to origin-destination IP traffic estimation using partial measurements
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Graph-Constrained Group Testing
IEEE Transactions on Information Theory
Boolean Compressed Sensing and Noisy Group Testing
IEEE Transactions on Information Theory
A power laws-based reconstruction approach to end-to-end network traffic
Journal of Network and Computer Applications
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Traffic matrix in a network describes the end-to-end network traffic which embodies the network-level status of communication networks from origin to destination nodes. It is an important input parameter of network traffic engineering and is very crucial for network operators. However, it is significantly difficult to obtain the accurate end-to-end network traffic. And thus obtaining traffic matrix precisely is a challenge for operators and researchers. This paper studies the reconstruction method of the end-to-end network traffic based on compressing sensing. A detailed method is proposed to select a set of origin-destination flows to measure at first. Then a reconstruction model is built via these measured origin-destination flows. And a purely data-driven reconstruction algorithm is presented. Finally, we use traffic data from the real backbone network to verify our approach proposed in this paper.