Upper Bound of Buffer Content Distribution for Self-Similar Traffic Models

Abstract

One of the most valuable results of traffic over the last years is the property of self-similarity in packet networks. In this paper, a self-similar traffic model driven by FBM is investigated. The estimation of upper bound probability buffer overflow for the traffic model is obtained. We also consider traffic that enters as input on some time-invariant linear system and find the estimation of buffer content distribution taking into account input traffic and response of the stochastic system. Finally, the obtained results in one particular case are applied.