The impact of non-Gaussian distribution traffic on network performance

Recent extensive measurements of real-life traffic demonstrate that the probability density function of the traffic is non-Gaussian. If a traffic model does not capture this characteristics, any analytical or simulation results will not be accurate. In this work, we study the impact of non-Gaussian traffic on network performance, and present an approach that can accurately model the marginal distribution of real-life traffic. Both the long- and short-range autocorrelations are also accounted. We show that the removal of non-Gaussian components of the process does not change its correlation structure, and we validate our promising procedure by simulations.