Abstract:
This paper discusses a new method based on the Empirical Mode Decomposition(EMD) algorithm to estimate the Hurst index that is an important statistical parameter of self-similar network traffic. The algorithm can adaptively decompose self-similar traffic into a series of Intrinsic Mode Function(IMF). By using the relationship between the energy of IMFs and the Hurst index, it can adaptively estimate the Hurst parameter of self-similar traffic. Experimental results show that this algorithm can adaptively estimate the Hurst index of self-similar traffic.
Key words:
self-similar,
Hurst index,
Empirical Mode Decomposition(EMD)
摘要: 针对表征自相似网络流量统计特性的赫斯特(Hurst)指数,讨论一种基于经验模式分解的Hurst指数估计算法。该算法通过对自相似网络流量数据进行自适应分解,得到一组满足指定余项误差的固有模态函数分量,由其能量对数化函数与Hurst指数之间的线性拟合,估计出Hurst指数。实验表明,该算法能对自相似网络流量的Hurst指数进行自适应估计。
关键词:
自相似,
赫斯特指数,
经验模式分解
CLC Number:
SHAN Pei-wei; LI Ming. Estimation of Hurst Index of Self-similar Traffic Based on EMD[J]. Computer Engineering, 2008, 34(23): 128-129,.
单佩韦;李 明. 基于EMD的自相似流量Hurst指数估计[J]. 计算机工程, 2008, 34(23): 128-129,.