On the modeling of network traffic and fast simulation of rare events using α-stable self-similar processes

We present a new model for aggregated network traffic based on α-stable self-similar processes which captures the burstiness and the long range dependence of the data. We show how the fractional Gaussian noise assumption fails and why our proposed model fits well by comparing real and synthesized network traffic. In addition, we show that we can speed up the simulation times for estimation of rare event probabilities, such as cell losses in ATM switches, by up to three orders of magnitude using α-stable modeling and importance sampling