The impact of non-Gaussian distribution traffic on network performance
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. This research was supported by the National Natural Science Foundation of China (NSFC) under grant No.69872025, the Natural Science Foundation of Tianjin under grant No.993800211 and the Natural Sciences and Engineering Research Council of Canada (NSERC) under grant No.OGP0042878.