Deriving traffic demands for operational IP networks: methodology and experience
IEEE/ACM Transactions on Networking (TON)
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
An information-theoretic approach to traffic matrix estimation
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Traffic engineering with estimated traffic matrices
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Structural analysis of network traffic flows
Proceedings of the joint international conference on Measurement and modeling of computer systems
Diagnosing network-wide traffic anomalies
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
The problem of synthetically generating IP traffic matrices: initial recommendations
ACM SIGCOMM Computer Communication Review
Providing public intradomain traffic matrices to the research community
ACM SIGCOMM Computer Communication Review
Compressed network monitoring for ip and all-optical networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
The Journal of Machine Learning Research
Traffic engineering with traditional IP routing protocols
IEEE Communications Magazine
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Most research on traffic matrices (TM) has focused on finding models that help with inference, but not with other important tasks such as synthesis of TMs, traffic prediction, or anomaly detection. In this paper we approach the problem of a general model for traffic matrices, and argue that such a model must be sparse, i.e., have a small number of parameters in comparison to the size of the TM. A Multi-Resolution Analysis (MRA) of TMs can provide such a sparse representation. The Diffusion Wavelet (DW) transform is a good choice as a MRA tool here, because it inherently adapts to the structure of the underlying network. The paper describes our construction of the two-dimensional version of the DW transform and shows how to use it for our proposed MRA of TMs. The results obtained with operational networks confirm the sparseness of the DW-based TM analysis approach and its applicability to other TM-related tasks.