Wavelets and Multifractals for Network Traffic Modeling and Inference
Vinay Ribeiro (vinay@rice.edu)
Electrical and Computer Engineering, Rice University
Rolf Riedi (riedi@rice.edu)
Electrical and Computer Engineering, Rice University
Richard Baraniuk (richb@rice.edu)
Electrical and Computer Engineering, Rice University
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 O(N) synthesis of
traffic for simulations and a tractable queuing analysis, thus
rendering it suitable for real networking applications including
end-to-end path modeling.