自相似业务:基于多分辨率采样和小波分析的Hurst系数估计方法

新近对局域网和广域网上大量突发业务流量的监测结果表明，采用自相似建模表征业务到达过程的长时间相关特性具有较高精度，其中Ｈｕｒｓｔ系数是表征业务突发特性的重要参数，因此在一定的观察时间内对突发业务的Ｈｕｒｓｔ系数进行快速、准确的估计是高速宽带网络（如ＡＴＭ）实施流量控制和缓冲资源分配的前提。本文提出一种基于多分辨率采样和小波分析的Ｈｕｒｓｔ系数快速估计方法，对严格二阶自相似模型下Ｈｕｒｓｔ系数的估计

Self-Similar Traffic:Hurst Parameter Estimation Based on Multiresolution Sampling and Wavelet Analysis

Recent measurement studies show that the burstiness of packet traffic in IAN as well as WAN is associated with long-range correlation that can be efficiently modeled as self-similar arrival process in terms ofaccuracy. Since Hurst index is the key value of this model replesenting the burstiness of traffic source , efficient estimation of Hurst index to the given accuracy is the basic step of flow control as well as buffer management in high speed broadband networks(e. g. ATM) with self-similar traffic. In this paper, we propose a fast Hurst index estimation method based on multiresolution sampling and wavelet analysis. The proposed method is the maximum likelihood estimator for strict self-similar model and can be used to check the consistency of Hurst index in different observed range of time. Simulation results based on fractal Gaussian noise and real traffic data reveal that the total number of count samples of the burst traffic for parameter estimation is peally reduced compared to the traditional methods such as R/S statistics and variance-time analysis. The proposed approach also shows more accuracy and robustness than traditional methods when lacking of sample data.Thus our method can be applied to the application of traffic enforcement and congestion control in ATM networks.