基于非平稳序列的主机负载预测及合成技术  

Prediction and Combination Method of Host Load Based on Non-Stationary Series

作　　者：姚淑萍[1] 胡昌振[1] 

机构地区：[1]北京理工大学软件学院,北京100081

出　　处：《北京理工大学学报》2007年第1期42-45,49,共5页Transactions of Beijing Institute of Technology

基　　金：国家部委基础科研项目(20021823)

摘　　要：提出一种新的主机负载表征指标———并发连接数,分析基于并发连接数的主机负载的自相似性和非平稳性,构建基于小波和支持向量回归的负载预测及合成算法.将主机负载序列进行多层小波分解与单支重构,低频信号采用AR模型预测,最小尺度高频信号采用加权移动平均方法预测,其它分支采用支持向量回归(SVR)预测;各信号预测值基于SVR方法加以合成,获得最终预测值.实验结果表明,将小波与支持向量回归应用于Web服务器负载预测的效果明显好于传统方法.A novel index-concurrent connection number was proposed to measure host load. Based on the new index, the self-similarity and non-stationary attributes of host load are analyzed and a novel prediction algorithm is constructed. This algorithm decomposes and reconstructs the host load series by wavelet into one low frequency signal in the largest scale and several high frequency signals at different scales. The low frequency signal is predicted with the AR model; the high frequency signal at the smallest scale with the weighed moving average method and the others with the support vector regression （SVR） models. After one-step-ahead prediction, the predicted results of these signals are combined into the final value based on SVR. Theoretical analysis and experimental results showed that this new algorithm can improve the predictive precision obviously.

关 键 词：并发连接数 主机负载 小波变换 支持向量回归 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]