Fast simulation for self-similar traffic in ATM networks

Abstract

Self-similar (or fractal) stochastic processes were proposed as more accurate models of certain categories of traffic (e.g., Ethernet traffic, variable-bit-rate video) which will be transported in ATM networks. Existing analytical results for the tail distribution of the waiting time in a single server queue based on fractional Gaussian noise and large deviation theory, are valid under a steady-state regime and for an asymptotically large buffer size. However, the predicted performance based on steady-state regimes may be overly pessimistic for practical applications. Theoretical approaches used to obtain the transient queueing behavior and queueing distributions for a small buffer size become quickly intractable. The approach we followed was based on fast simulation techniques for the study of certain rare events such as cell losses with very small probability of occurrence. Our simulation experiments provide an insight on the transient behavior that is not possible to predict using current analytical results. Finally they show good agreement with existing results when approaching steady-state.