A predictability analysis of network traffic 3

8 This paper assesses the predictability of network traffic by considering two metrics: (1) how far into the future a traffic 9 rate process can be predicted with bounded error; (2) what the minimum prediction error is over a specified prediction 10 time interval. The assessment is based on two stationary traffic models: the auto-regressive moving average and the 11 Markov-modulated poisson process. In this paper, we do not aim to propose the best traffic (prediction) model, which is 12 obviously a hard and arguable issue. Instead, we focus on the constrained predictability estimation with assumption 13 and discussion about the modeling accuracy. The specific time scale or bandwidth utilization target of a predictive 14 network control actually forms the constraint. We argue that the two models, though both short-range dependent, can 15 capture statistics of (self-similar) traffic quite accurately for the limited time scales of interests in measurement-based 16 traffic management. This argument, in mathematical terms, simply reflects the fact that the summarized exponential 17 (correlation) functions may approximate a hyperbolical one very well. 18 Our study reveals that the applicability of traffic prediction is limited by the deteriorating prediction accuracy with 19 increasing prediction interval. From both analytical and numerical studies, we explore the different roles of traffic 20 statistics, either at the 1st-order or the 2nd-order, in traffic predictability. Particularly, the statistical multiplexing and 21 proper measurement (e.g. sampling/filtering) of traffic show positive effects. Experimental results suggest promising 22 backbone traffic prediction, and generally enhanced predictability if small time-scale traffic variations, which are usually 23 of less importance to bandwidth allocation and call admission control, have been filtered out. The numerical results in 24 the paper provide quantized reference to the optimal online traffic predictability for network control purposes. 2002 25 Published by Elsevier Science B.V.