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
Power laws and the AS-level internet topology
IEEE/ACM Transactions on Networking (TON)
Structural analysis of network traffic flows
Proceedings of the joint 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
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
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
Structural analysis of network traffic matrix via relaxed principal component pursuit
Computer Networks: The International Journal of Computer and Telecommunications Networking
IEEE Transactions on Signal Processing
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
A compressive sensing-based reconstruction approach to network traffic
Computers and Electrical Engineering
A measurement-based study on the correlations of inter-domain Internet application flows
Computer Networks: The International Journal of Computer and Telecommunications Networking
Hi-index | 0.00 |
To obtain accurately end-to-end network traffic is a significantly difficult and challenging problem for network operators, although it is one of the most important input parameters for network traffic engineering. With the development of current network, the characteristics of networks have changed a lot. In this paper, we exploit the characteristics of origin-destination flows and thus grasp the properties of end-to-end network traffic. An important and amazing find of our work is that the sizes of origin-destination flows obey the power laws. Taking advantage of this characteristic, we propose a novel approach to select partial origin-destination flows which are to be measured directly. In terms of the known traffic information, we reconstruct all origin-destination flows via compressive sensing method. In detail, here, we combine the power laws and the constraints of compressive sensing (namely restricted isometry property) together to build measurement matrix and pick up the partial origin-destination flows. Furthermore, we build a reconstruction model from the known information corresponding to compressive sensing reconstruction algorithms. Finally, we reconstruct all origin-destination flows from the observed results by solving the reconstruction model. And we provide numerical simulation results to validate the performance of our method using real backbone network traffic data. It illustrates that our method can recover the end-to-end network traffic more accurately than previous methods.