摘要
Recent extensive measurements of real-life traffic demonstrate that the probability density function of the traffic in 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.
