Modeling Self-Similar Traffic through Markov Modulated Poisson Processes over Multiple Time Scales

Abstract. In recent years several studies have reported peculiar types of traffic behavior, such as long-range dependence and self-similarity, which can have significant impact on network performance. In this paper we propose a novel traffic model and parameter fitting procedure, based on Markov Modulated Poisson Processes (MMPPs), which is able to capture variability over many time scales, a characteristic of self-similar traffic. The fitting procedure matches the complete distribution at each time scale, and not only some of its moments as it is the case in related proposals. Our results show that the proposed traffic model and parameter fitting procedure closely matches the main characteristics of measured traces over the time scales present in data