Global Modeling and Prediction of Computer Network Traffic

TL;DR

This paper introduces a probabilistic framework for modeling and predicting global computer network traffic, capturing complex behaviors and enabling inference on unobserved links, with applications in anomaly detection and network management.

Contribution

It presents a novel probabilistic model integrating link-level traffic with routing, validated on real data, for improved traffic prediction and anomaly detection.

Findings

Model accurately captures traffic fluctuations.

Predicts unobserved link traffic from limited data.

Applicable to anomaly detection and network management.

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.