Wavelet multifractal modeling for network traffic and queuing analysis

We give a wavelet-based model of network traffic, which exhibits multifractal characters. The discovery of the multifractal nature of traffic has made new models and analysis tools for traffic essential, since self-similar or long-range dependence (LRD) models are far too optimistic in their predictions of performance. The short-range dependence (SRD) must be considered especially in the flow control and CRC design. We find that the wavelet-based model can capture important fractal properties like multi-scale variability and bursting that affect the performance. The experiments and queuing analysis were performed and the results obtained show that the model agrees with the real data.