Load prediction of grid computing resources based on ARSVR method  

基于ARSVR方法的网格计算资源负载预测(英文)

作　　者：黄刚[1] 王汝传[1] 解永娟[1] 石小娟[1] 

机构地区：[1]南京邮电大学计算机学院,南京210003

出　　处：《Journal of Southeast University(English Edition)》2009年第4期451-455,共5页东南大学学报（英文版）

基　　金：The National High Technology Research and Development Program of China (863 Program) (No2007AA01Z404)

摘　　要：Based on the monitoring and discovery service 4 （MDS4） model, a monitoring model for a data grid which supports reliable storage and intrusion tolerance is designed. The load characteristics and indicators of computing resources in the monitoring model are analyzed. Then, a time-series autoregressive prediction model is devised. And an autoregressive support vector regression（ ARSVR） monitoring method is put forward to predict the node load of the data grid. Finally, a model for historical observations sequences is set up using the autoregressive （AR） model and the model order is determined. The support vector regression（SVR） model is trained using historical data and the regression function is obtained. Simulation results show that the ARSVR method can effectively predict the node load.在MDS4监控模型的基础上,设计了基于可靠存储与容侵数据网格的监控模型,分析了监控模型中计算资源的负载特性、指标.然后,设计了基于SVR的时间序列自回归预测模型,提出了用于数据网格负载预测的监控ARSVR方法.最后,利用AR模型对历史观测序列进行建模,确定模型的阶次.根据历史数据对SVR进行训练,得到回归函数.仿真实验结果表明,ARSVR方法能对节点的负载进行有效预测.

关 键 词：GRID autoregressive support vector regression algorithm computing resource load prediction 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]