Deriving traffic demands for operational IP networks: methodology and experience
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
Trajectory sampling for direct traffic observation
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
Traffic matrix estimation: existing techniques and new directions
Proceedings of the 2002 conference on Applications, technologies, architectures, and protocols for computer communications
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 matrix estimation on a large IP backbone: a comparison on real data
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
MPLS and traffic engineering in IP networks
IEEE Communications Magazine
IEEE Communications Magazine
NetScope: traffic engineering for IP networks
IEEE Network: The Magazine of Global Internetworking
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Traffic matrix information is very important to networks. In this paper, a traffic matrix model is proposed based on passive measurement that can be used to high-speed IP network. The core of model has three parts as follows: 1) measuring traffic at the edge node of network. The passive measurement method is introduced to measure the node traffic based on software measurement. Because the software is based on flow measurement, the flow matching, that is, packet classification is a key problem. In packet classification, the dual hash algorithm is proposed. The algorithm is introduced based on the non-collision hash and XOR hash. 2) introducing non-intrusive measurement method to acquire path information and then the sampling method is introduced. In this method, the path information is writen in the flag field. 3) deducing sampling probability so that the point of optimization is selected. Simulation results prove the effectiveness of this algorithm.