Nonstationarity of network traffic within multi-scale burstiness constraint

The scaling behavior has been discovered in the past decade, which has provided hope that mathematical models can be found to describe the nature of the traffic. Similarly to long-range dependence (LRD), nonstationarity is also one of vital characteristics of network traffic. In this paper, a novel traffic model is proposed based on that the traffic aggregation behavior is abstracted in hierarchical way. The traffic model is focused on the burst traffic rate. Firstly, the burst size of output aggregated flow by edge device of a network domain is derived by pseudo queue system methods. And, the nonstationarity of input traffic is developed by a generalized fractal Gaussian noise process, which is constructed by a large number of train traffic series. They are Poisson arrival and their lifetime is exponential distribution. The model has a good performance of fitting to real traffic data within multi-scale for long time, which is illuminated by simulated results.