Modelling the self-similar behaviour of network traffic

Traditional Poisson or Markovian traffic models have been proven, by several studies, inappropriate for predicting the performance of networks with long-range dependent traffic. Thus, the use of conventional call admission control algorithms based on short-range dependent models cannot achieve the quality of service required by self-similar sources, since network performance is actually worse in terms of buffer overflow probability and delay. The variety of traffic types that depict long-range dependence creates the need for new models suitable for network design and congestion control mechanisms. In this paper, we present a call admission control (CAC) scheme for traffic with selfsimilar behaviour based on the notion of effective bandwidth. Our solution satisfies the quality of service requirements of the source, expressed in terms of buffer overflow probability, while taking into account the long-range dependence property of the traffic through the Hurst parameter. We base our CAC mechanism on a Gaussian model initially studied by Norros. We show that this model performs well in the case of one source as well as in the case of many aggregated sources. Our scheme assumes homogenous sources, each one represented by a fractional Gaussian noise process characterised by the mean bit rate and the self-similarity parameters (H,alpha). We study the estimation of (H,alpha) from the real traffic and show the connection between their estimates. Finally, we present a way to calculate the equivalent capacity, which is necessary so that the aggregated stream will achieve the desired loss rate. In our simulation experiments, we used data collected from an IP over ATM high-speed link, connecting the National Research Network of Greece (GRnet) to the Trans-European network backbone (TEN-34), a part of the global Internet. (C) 2000 Published by Elsevier Science B.V. All rights reserved.