A Large-Scale Distributed Traffic Matrix Estimation Algorithm

Jian Ni, Sekhar Tatikonda, Edmund M. Yeh

Abstract: As today's communication networks (e.g., the Internet) grow in size and diversity, accurate, large-scale, and distributed traffic matrix estimation techniques will become increasingly important for many network control and management tasks. In this paper, we study the gravity model with entropy penalization approach for estimating traffic matrices based on link traffic measurements, which is known to have remarkable accuracy for real networks [7], [12]. We propose a dual approach to convert the constrained primal optimization problem under the gravity model into an unconstrained dual optimization problem. For most practical networks in which the number of links is much smaller than the number of origin-destination pairs, the dual problem has a much smaller dimension and hence scales for large networks. In addition, the solution algorithm for the dual problem can be implemented in a distributed manner.