Network heavy traffic modeling using α-stable self-similar processes

We propose a new model for network heavy-traffic approximation, based on alpha -stable self-similar processes, namely the skewed linear fractional stable noise. The model demonstrates more flexibility than existing models in fitting different levels of burstiness and correlation in the data. Nonetheless, it is parsimonious in the number of parameters, which have a direct physical meaning. An algorithmic procedure for the estimation of the model parameters is presented, and an asymptotic lower bound of the residual queueing distribution is derived. Extensive simulations are presented, where the new model is fitted to bursty Ethernet data collected at Bellcore (now Telcordia) Laboratories. Furthermore, new measurements of aggregate web and webcasting traffic are introduced along with traffic generated by the fitted new model. Queueing simulations of a G/D/1 system confirm our analytical results regarding the tail of the queue distribution.