Performance prediction for Grid workflow activities based on features-ranked RBF network

作     者：王洁 Duan Rubing Farrukh Nadeem 

作者机构：Institute of Computing Technology Chinese Academy of Sciences Beijing 100050 P.R. China Institute of Computer Science University of Innsbruck Innsbruck Austria Graduate University of Chinese Academy of Sciences Beijing 100049 P.R. China 

出 版 物：《High Technology Letters》 (高技术通讯（英文版）)

年 卷 期：2009年第15卷第2期

页      面：203-207页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by the European Union through the IST-034601 edutain@grid project 

主　　题：performance prediction radial basis function （RBF） neural network features rank Grid workflow activities 

摘      要：Accurate performance prediction of Grid workflow activities can help Grid schedulers map activitiesto appropriate Grid *** paper describes an approach based on features-ranked RBF neural networkto predict the performance of Grid workflow *** results for two kinds of real worldGrid workflow activities are presented to show effectiveness of our approach.