网络流量长相关特性的滑窗时变估计算法

A Long-Range Dependence Sliding Window Time-Varying Estimation Algorithm for Network Traffic

网络流量在动态演进过程中呈现出长相关(LRD)特性,定量描述LRD特性是网络行为研究的重要问题之一.由于传统LRD估计算法采用全域求和平均,造成序列中突发信息损失,致使传统算法均不能在复杂条件下有效估计LRD.在引入时变Hurst指数函数的概念后,提出了时域滑窗时变Hurst(SWTV-H)估计算法.SWTV-H算法在某一分辨率水平上给出局域内Hurst指数的估计,并通过局域时移实现流量序列全域内LRD趋势的动态估计.分别用仿真以及真实网络流量数据对其有效性进行了验证,与传统算法的估计结果相比,SWTV-H算法能更准确估计LRD特性,且具有更好的鲁棒性.

Long-range dependence （LRD） of network traffic is revealed in a dynamical evolution way, Thus, quantifying the LRD characteristics is one of the vital problems to study network behavior. Traditional LRD estimators can not give the accurate estimation under some complex conditions after seven type traditional LRD estimators are comprehensively evaluated in this paper, The main reason is that the traditional methods introduce the smoothness to traffic series in some degreedues to doing average within global domain. Consequently, some important features of network traffic such as burstiness and LRD are destroyed. A sliding window time-varying Hurst （SWTV-H） exponent estimation algorithm for LRD characteristics is proposed to improve the Hurst exponent estimating performance based on the concept of time-varying Hurst exponent induced. The SWTV-H algorithm can estimate the local Hurst exponent in some resolution ratio level, and provide a dynamic estimation of LRD trend of global behavior by shifting the local domain. The effectiveness of the SWTV-H algorithm is validated by the data of the artificial fractal Gaussian noise （fGn） series and the actual network traffic series. The results indicate that the SWTV-H algorithm is more accurate and reliable to estimate LRD characteristics compared with the traditional methods, and it has robust performance.