摘要
基于小波多尺度的分解和重构思想,将网络流量通过小波分解成不同尺度下的逼近信号和细节信号,然后分别单支重构成低频序列和高频序列。根据低频序列和高频序列的不同特性,分别采用自回归模型(AR)和线性最小均方误差估计(LMMSE)对未来网络流量进行预测,最后重新组合生成预测流量,通过对真实网络流量的仿真实验,结果显示该方法能比较准确地预测未来的网络流量。
Theory of decomposition and reconstruction based on multi-scale of wavelet, the network traffic, which were decomposed an approximation signal and some detail sigals of different scales. Then these signals were reconstructed into several low frequency and high frequency time serials by wavelet. These serials were predicted by LMMSE and AR models respectively according to their different features. The predicted results of all serials were combined into the final prediction traffic. The simulation results with the real traffic traces show the method can predict future traffic as well as the accuracy and efficiency.
出处
《微电子学与计算机》
CSCD
北大核心
2008年第1期130-133,共4页
Microelectronics & Computer
基金
国防基础研究基金项目(A1420061266)
