Point Process Approaches for Modeling and Analysis of Self-similar Traac: Part Ii { Applications

In previous work 24], Fractal Point Processes (FPPs) have been proposed as novel tools for understanding, modeling and analyzing diverse types of self-similar traac behavior. We apply the FPP models in the context of network traac modeling and performance analysis. Two qualitatively diierent fractal data sets (Bellcore Ethernet traces) are characterized by FPP models. Comparison of model-driven and trace-driven queueing simulation results show that the matched models yield close agreement with the traces over a wide range of system parameters. We also show that under suitable conditions, the FPP models yield Gaussian processes. Queueing simulation shows that the FPP models can be computationally eecient alternatives for generating fractional Gaussian noise processes. Finally, we divide fractal traac into two types, application-level fractal traac and network-level fractal traac, and argue that each type has radically diierent implications for the design and control of future emerging broadband networks depending on the Quality of Service (QoS) requirements. We present our viewpoints on implications of each type of fractal traac and roles of successful fractal models for the design and control of future emerging broadband networks.