Quick Traffic Matrix Estimation Based on Link Count Covariances

Abstract

In this paper we consider the problem of traffic matrix estimation. As the problem is underconstrained, some additional information has to be brought in to obtain a solution. If we have a sequence of link count measurements available, a natural candidate is to use the link count sample covariance matrix under the assumption of a functional relationship between the mean and the variance of the traffic. We propose two computationally light-weight methods for traffic matrix estimation based on the covariance matrix, the projection method and constrained minimization method. The accuracy of these methods is compared with that of other methods using second order moment estimates by simulation under synthetic traffic scenarios.