A multifractal wavelet model for the generation of long-range dependency traffic traces with adjustable parameters

• We proposed a novel algorithm to generate multifractal traffic finite-length traces.
• The algorithm is based on the MultiFractal Hurst model and Multifractal Wavelet Model.
• To validate the algorithm, a trace with specific parameters values is synthetized.
• The main contribution is to allow adjusting the Hurst parameter and spectrum width.

The available multifractal traffic finite-length time series to implement performance test of the management, control and admission algorithms, and level of service about M/M/1 models for WAN/LAN communication systems are very few and their recollection through current mechanisms is very slow due to the amount of data that must be obtained. Hence, it is necessary to develop a tool which synthesizes traces with multifractal features and allows the stochastic parameters configuration as its average, Hurst parameter and, multifractal spectrum width. This article describes the development of a proposed algorithm to generate multifractal traffic finite-length time series with a Hurst parameter and the multifractal spectrum width, sampling and adjustable, called MultiFractal Hurst Spectrum Width (MFHSW). The MFHSW algorithm is based on the MultiFractal Hurst model (MFH) and on the Multifractal Wavelet Model (MWM), to construct the time series through a binomial multiplicative cascade. The main contribution of the MFHSW algorithm is to allow adjusting both the Hurst parameter and the multifractal spectrum width, the aforementioned is achieved by appropriately modifying the beta distributions that conform the binomial cascade. Consequently, the impact developed by the algorithm to the trace generation with multifractal features will be the improvement in the simulation and data network modeling.The MFHW algorithm behaves as an expert system when inferring to distribution of the beta coefficients present in the scales that make part of the binomial cascade starting from the stochastic parameters configured by the user, and obtaining the corresponding time series through an inference engine. To validate the algorithm effectiveness, a trace with the Hurst parameter sampling and the multifractal spectrum width similar to the presented in a network traffic time series are synthetized. The MFHSW happens to be a promising tool for the modeling of time series applicable to diverse fields as the traffic engineering, finances, biomedical signals, among other real traces with multifractal features.