A novel traffic model based on wavelet analysis

Recent research show network traffic exhibits drastically different statistics according to scales. At large time scales, there are nearly Gaussian marginals and long range dependence, and there are highly non-Gaussian marginals and multifractal scaling on small scales. Structure network researchers show there are two components of the traffic according to the speed of connections, one component holds most traffic and being mostly Gaussian, the order absorbs virtually all the small scale bursts. For better understanding of this phenomenon, we propose a novel traffic model based on wavelet. Simulation results with the real traffic show this model is flexible and parsimonious to accommodate Gaussian as well as bursty behavior on different scales.