A Long-Range Dependent Workload Model for Packet Data Traffic

We consider a probabilistic model for workload input into a telecommunication system. It captures the dynamics of packet generation in data traffic as well as accounting for long-range dependence and self-similarity exhibited by real traces. The workload is found by aggregating the number of packets, or their sizes, generated by the arriving sessions. The arrival time, duration, and packet-generation process of a session are all governed by a Poisson random measure. We consider Pareto-distributed session holding times where the packets are generated according to a compound Poisson process. For this particular model, we show that the workload process is long-range dependent and fractional Brownian motion is obtained as a heavy-traffic limit. This yields a fast synthesis algorithm for generating packet data traffic as well as approximating fractional Brownian motion.