Global Modeling and Prediction of Computer Network Traffic
Abstract
We develop a probabilistic framework for global modeling of the traffic over a computer network. This 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 simulated and real data. It is then applied to predict traffic fluctuations over unobserved links from a limited set of observed links. Further, applications to anomaly detection and network management are briefly discussed.
Cite
@article{arxiv.1005.4337,
title = {Global Modeling and Prediction of Computer Network Traffic},
author = {Stilian A. Stoev and George Michailidis and Joel Vaughan},
journal= {arXiv preprint arXiv:1005.4337},
year = {2010}
}