Abstract: The measurement studies show that the burstiness of packet traffic in LAN as well as WAN is associated with self－similar and long－range dependency,and Hurst parameter is the key value of this model representing the burstiness of traffic.An adaptive,efficient unbiased estimator of Hurst index based on the lifting scheme　for wavelet transform and correlation coefficient is presented.Compared with the existing wavelet－based estimator,the new method performs inplace computation and reduces the　computational complexity by about half.Simulation results based on fractal Gaussian noise and real traffic data reveal the proposed approach shows more adaptiveness,accuracy and robustness than traditional estimators.Thus this estimator can be applied to traffic management and real－time control in high－speed networks.

摘要： 对局域网和广域网上大量突发网络流量的分析结果表明,网络流量普遍存在着自相似性和长相关性,其中赫斯特指数是表征网络流量突发性的重要参数。以小波提升框架为基础,结合相关系数分析法,给出了自适应的赫斯特指数估计方法,与传统的小波估计法相比,该法执行原位计算,使计算复杂性减少了约一半,同时该方法在一般意义上是无偏的。分形高斯噪声和真实突发网络数据的仿真结果均表明,自适应方法比传统估计方法具有更高的估计精度,能够自适应地选择最优尺度区间,因此可望应用于高速网络的网络管理和实时控制。