By Topic

Grid Resource Prediction Based on Support Vector Regression and Genetic Algorithms

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Liang Hu ; Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China ; Guosheng Hu ; Kuo Tang ; Xilong Che

In order to manage the grid resources more effectively and provide a more suitable task scheduling strategy, the prediction information of grid resources is necessary in the grid system. In this study, support vector regression (SVR), which is a novel and effective regression algorithm, is applied to grid resource prediction. In order to build an effective SVR model, SVR's parameters must be selected carefully. Therefore, we develop a genetic algorithm-based SVR (GA-SVR) model that can automatically determine the optimal parameters of SVR with higher predictive accuracy and generalization ability simultaneously. This study pioneered on employing genetic algorithm to optimize the parameters of SVR for grid resource prediction. The performance of the hybrid model (GA-SVR), the back-propagation neural network (BPNN) and traditional SVR model whose parameters are obtained by trial-and-error procedure (T-SVR) have been compared with benchmark data set. Experimental results demonstrate that GA-SVR model works better than the other two models.

Published in:

2009 Fifth International Conference on Natural Computation  (Volume:1 )

Date of Conference:

14-16 Aug. 2009