分数阶Fourier变换域中网络流量的自相似特性分析

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

通过分析网络流量数据在FrFT域的统计特性发现,实际网络流量在FrFT域满足自相似性,进一步地,针对网络流量在FrFT域的"时域"和"频域"展开,分别给出了基于改进的整体经验模态分解—去趋势波动分析(MEEMD-DFA)的Hurst指数估计法以及基于加权最小二乘回归(WLSR)的Hurst指数自适应估计法。实验结果表明,相比于现有估值算法,MEEMD-DFA法具有较高的估计精度,但计算复杂度高;而FrFT自适应估计法则具有更优的估计顽健性,且计算复杂度较低,可作为一种实时在线估计真实网络数据Hurst指数的方法。

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.