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
An information-theoretic approach to traffic matrix estimation
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
A distributed approach to measure IP traffic matrices
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
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
Fisher information of sampled packets: an application to flow size estimation
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
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Traffic matrix estimation has caught numerous attentions these days due to its importance on network management tasks such as traffic engineering and capacity planning for Internet Service Providers (ISP). Various estimation models and methods have been proposed to estimate the traffic matrix. However, it is difficult to compare these methods since they adopt different model assumptions. Currently most evaluations are based on some particular realization of data. We propose to use the (Bayesian) Cramér-Rao Bound (CRB) as a benchmark on these estimators. We also derive the maximum likelihood estimator (MLE) for certain models. With coupled mean and variance, our simulations show that the least squares (LS) estimator reaches the CRB asymptotically, while the MLEs are difficult to calculate when the dimension is high.