Analysis the self-similarity of network traffic in fractional Fourier transform domain

Abstract

Statistical characteristics of network traffic data in FrFT domain were analyzed,which indicates the self-simi-larity feature.Further,Hurst parameter estimation methods based on modified ensemble empirical mode decomposi-tion-detrended fluctuation analysis (MEEMD-DFA) and adaptive estimator with weighted least square regression (WLSR) were presented,which aimed at displaying network traffic in “time” or “frequency” domain of FrFT domain separately.Experimental results demonstrate that the MEEMD-DFA method has more accurate estimate precision but higher com-putational complexity than existing common methods.The overall robustness of adaptive estimator is more satisfactory than that of the other methods in simulation,while it has lower computational complexity.Thus,it can be used as a real-time online Hurst parameter estimator for traffic data.

Keywords

• self-similarity;fractional Fourier transform;Hurst parameter;ensemble empirical mode decomposition;weighted least square regression;adaptive