基于分数布朗运动的自相似流量判别及生成方法

Self-similar traffic discrimination and generating methods based on fractal Brown motion

针对网络流量自相似程度判别方法较少和应用分数布朗运动(FBM)进行自相似流量模拟时可能会产生负值流量等问题,给出一种基于多阶矩的自相似流量判别方法和改进FBM模型的自相似流量模拟方法。首先通过分析样本矩的数学式,在分形矩分析的基础上得到一种多阶矩的自相似判别方法,然后对经典的随机中点置位(RMD)算法进行改进,最后对Bellcore和LBL实验室采集的真实流量数据进行自相似判别和模拟,仿真验证实验结果表明该方法的有效性。

To deal with the difficulties of lacking the discrimination method of network＇s traffic self-similarity and producing negative traffic based on classical Fractal Brown Motion（FBM）,a discrimination method was proposed based on multiple order moment and a generation method was provided based on modified FBM model.Firstly,the mathematical formula of sample moment was studied.The discrimination method of self-similarity traffic was obtained on account of fractal moment analysis.Secondly,the classical Random Midpoint Displacement（RMD） algorithm was modified.At last,taking account of the real traffic of Bellcore and LBL,the discrimination method and generation method were given.The comparison of the simulation results with the actual experimental data proves that the method is feasible.