A MARKOVIAN APPROACH FOR MODELING PACKET TRAFFIC WITH LONG-RANGE DEPENDENCE

We present a simple Markovian framework for modeling packet traffic wi th variability over several time scales. We present a fitting procedur e for matching second-order properties of counts to that of a second-o rder self-similar process. Our models essentially consist of superposi tions of two-state MMPP's. We illustrate that a superposition of four two-state MMPP's suffices to model second-order self-similar behavior over several time scales. Our modeling approach allows us to fit to ad ditional descriptors while maintaining the second-order behavior of th e counting process. We use this to match interarrival time correlation s.