A Predictability Analysis of Network Traffic

This paper assesses the predictability of network traffic by considering two metrics: (1) how far into the future a traffic rate process can be predicted with bounded error; (2) what the minimum prediction error is over a specified prediction time interval. The assessment is based on two stationary traffic models: the auto-regressive moving average and the Markov-modulated poisson process. In this paper, we do not aim to propose the best traffic (prediction) model, which is obviously a hard and arguable issue. Instead, we focus on the constrained predictability estimation with assumption and discussion about the modeling accuracy. The specific time scale or bandwidth utilization target of a predictive network control actually forms the constraint. We argue that the two models, though both short-range dependent, can capture statistics of (self-similar) traffic quite accurately for the limited time scales of interests in measurement-based traffic management. This argument, in mathematic...

• Type: Conference
• Year: 2000
• Where: INFOCOM
• Authors: Aimin Sang, San-qi Li
• Keywords: Communications, INFOCOM 2000, Traffic Predictability, Traffic Prediction, Traffic Rate Process