As a follower to our papers ("Blind maximum likelihood estimation of traffic matrices under long-range dependent traffic" and "Estimation of traffic matrices in the presence of long memory traffic") on traffic analysis in the presence of long-range dependence, our survey paper "Estimation of Traffic Matrices for LRD Traffic" has been published in the edited volume "Complex Models and Computational Methods in Statistics" by Springer.
Abstract: The estimation of traffic matrices in a communications network on the basis of a set of traffic measurements on the network links is a well known problem, for which a number of solutions have been proposed when the traffic does not show dependence over time, as in the case of the Poisson process. However, extensive measurements campaigns conducted on IP networks have shown that the traffic exhibits long range dependence. Here two method are proposed for the estimation of traffic matrices in the case of long range dependence, their asymptotic properties are studied, and their relative merits are compared.
