基于小波的Hurst指数自适应估计方法

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Hurst指数是表征网络流量突发特性的重要参数.通过在小波域内对网络流量这种特性的分析,给出了其小波系数的本质和统计特性.针对基于小波的Hurst指数估计方法的自适应问题,结合方差分析给出了一种有效的解决方法,从而提出了自适应的参数估计方法,并且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,而且仅具有O(N)的计

Adaptive Hurst Index Estimator Based on Wavelet

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 index is the key value of this model representing the burstiness of traffic. With the analysis in discrete wavelet domain, the nature of the wavelet coefficients and their statistical properties are proposed. Then an adaptive, efficient unbiased estimator of Hurst index based on multiresolution wavelet analysis and weighted regression is presented. Simulation results based on fractal Gaussian noise and real traffic data reveal the proposed approach shows more adaptiveness, accuracy and robustness than traditional estimators which has only O(N) computation. Thus this estimator can be applied to the application of traffic management and real-time control in high-speed networks.