Use of α-stable self-similar stochastic processes for modeling traffic in broadband networks

Abstract

In this article, we propose a new model for aggregate network traffic. This model, besides reflecting self-similarity and long-range dependence, is able to capture the appropriate level of burstiness of different types of traffic by selecting the proper parameters. Different types of self-similar traffic traces (LAN/WAN, WWW, VBR video) are analysed by estimating their self-similarity coefficient H, as well as the parameters of their marginal distributions. When comparing the real traces with our artificial traces, the agreement, which was evaluated both qualitatively (visually) and quantitatively (by means of the marginal CDF and the periodogram), is better than that achieved with previously proposed models. By analysing different types of traffic traces, the model is shown to be flexible enough to be applied to a variety of communications scenarios. A queue with our proposed traffic as input is analysed. A proof of convergence of aggregate traffic to α-stable processes is also included, as well as the conditions under which the Gaussian assumption is appropriate.