Wavelets and Multifractals for Network Traffic Modeling and Inference
Ribeiro, Vinay Joseph
Riedi, Rudolf H.
Baraniuk, Richard G.
This paper reviews the multifractal wavelet model (MWM) and its applications to network traffic modeling and inference. The discovery of the fractal nature of traffic has made new models and analysis tools for traffic essential, since classical Poisson and Markov models do not capture important fractal properties like multiscale variability and burstiness that deleteriously affect performance. Set in the framework of multiplicative cascades, the MWM provides a link to multifractal analysis, a natural tool to characterize burstiness. The simple structure of the MWM enables fast synthesis of traffic for simulations and a tractable queuing analysis, thus rendering it suitable for real networking applications including end-to-end path modeling.