Wavelet multifractal modeling for network traffic and queuing analysis

In this paper, we give a wavelet-based model of network traffic, which exhibits multifractal character 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. Short-range Dependence (SRD) is must be considered especially in flow control (FC) and CRC design. We find that the wavelet-based model can capture important fractal properties like multi-scale variability and bursting that deleteriously affect performance. Experiments and queuing analysis is performed and the results show that the model is matched well with the real data.