|
|||||
|
|
Performance prediction for Grid workflow activities based on features-ranked RBF network |
|
| Authors: | Wang Jie Duan Rubing Farrukh Nadeem |
| Affiliation: | 1. Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100080,P.R.China;Institute of Computer Science,University of Innsbruck,Innsbruck,Austria;Graduate University of Chinese Academy of Sciences,Beijing 100049,P.R.China 2. Institute of Computer Science,University of Innsbruck,Innsbruck,Austria |
| Abstract: | Accurate performance prediction of Grid workflow activities can help Grid schedulers map activities to appropriate Grid sites.This paper describes an approach based on features-ranked RBF neural network to predict the performance of Grid workflow activities.Experimental results for two kinds of real world Grid workflow activities are presented to show effectiveness of our approach. |
| Keywords: | performance prediction radial basis function (RBF) neural network features rank Grid workflow activities |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |