Aggregated self-similar wireless traffic properties analyses based on Sup-FRPP model

Abstract

In this paper, superposition fractal renewal point process (Sup-FRPP) is applied to analyze the average arrival rate, Hurst parameter and the fractality start-time of the aggregated self-similar traffic. Simulation results demonstrate that the aggregated multiple self-similar traffic streams also exhibits self-similarity, which actually intensifies rather than diminishes burstiness of single self-similar traffic stream. The burstiness can have detrimental effects on the network performance. Thus these results are very useful for forecasting network traffic variation, optimizing bandwidth allocation and guaranteeing network quality of service (QoS).