Abstract:

In order to characterize local features of network redundant traffic on small-time scale more accurately, a new Cauchy-Laplace multifractal wavelet model was proposed. An algorithm for estimating parameters of wavelets was also put forward. A joint distribution function was adopted to describe local features, i.e., Cauchy and Laplace distributions were used to obtain the parameter multiply factors for heavy-tailed and spike features, respectively. A threshold for ratio of wavelets to scaling parameters, which decides how these two distributions affected redundant traffic modeling, was achieved by probability comparison. Experiments show that the proposed model can well characterize small-time scale multifractal features of network redundant traffic.

Key words: redundant traffic, spike feature, heavy-tailed feature, Cauchy-Laplace multifractal wavelet model