Performance Modeling and Network Management for Self-similar Traffic

Abstract

Since the discovery of the self-similar nature of network traffic, researchers were able to propose new traffic models [Mayor96d, Norros94] that are better able to mimic the long-range dependence phenomenon exhibited by real network traffic. Nevertheless, since most of the existing queueing theory is based on the assumption of Markovian models, there are few analytical results dealing with an ATM queueing system driven by a self-similar process [Addie95b, Duffield95, Likhanov95, Mayor96d, Parulekar96, Ryu96a]. In this work, we give an overview of traffic models and analytical tools capable of computing tail probabilities of an ATM queueing system driven by a self-similar process. We also explain the meaning of long-range dependence and its impact on network performance and network management protocols, by revisiting Mandelbrot’s work[Mandelbrot69]. We propose a traffic characterization based on a fractional Brownian motion envelope process. By using this characterization, we show a framework derived in [Mayor96d] capable of computing bandwidth and buffer requirements in ATM networks driven by aggregate, heterogeneous, self-similar processes.KeywordsSelf-similarATM envelope process and fractional brownian motion