Abstract not found

Following our work described in Part I of this paper that modeled various correlation structures of Gaussian traffic in wavelet domain, we extend our previous models to heterogeneous network traffic with either a non-Gaussian distribution or a periodic structure. To include a non-Gaussian distribution, we first investigate what higher-order statistics are pertinent by exploring a relationship between time-scale analysis of wavelets and cumulative traffic. We then develop a novel algorithm in the wavelet domain to capture the important statistics. By utilizing local properties of wavelet basis in both space and time, we further extend such wavelet models to periodic MPEG traffic. As wavelets provide a natural fit to higher-order statistics as well as localized spatial and temporal dependence of periodic traffic at different time scales, the resulting wavelet models for both non-Gaussian and periodic traffic are simple and accurate with the lowest computational complexity attainable. 1 I...

In this paper we examine the usefulness of the M/Pareto process as a model for broadband traffic. We show that the queueing performance of the M/Pareto process depends upon the level of aggregation in the process. When the level of aggregation is high, the M/Pareto converges to a long range dependent Gaussian process. For lower levels of aggregation, the M/Pareto is capable of modeling the queueing performance of real broadband traffic traces.