On Global Modeling of Backbone Network Traffic

We develop a probabilistic framework for global modeling of the traffic over a computer network. The model integrates existing single-link (-flow) traffic models with the routing over the network to capture the global traffic behavior. It arises from a limit approximation of the traffic fluctuations as the time-scale and the number of users sharing the network grow. The resulting probability model is comprised of a Gaussian and/or a stable, infinite variance components. They can be succinctly described and handled by certain ´space-time´ random fields. The model is validated against real data and applied to predict traffic fluctuations over unobserved links from a limited set of observed links.