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
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