Support vector regression and ant colony optimization for grid resources prediction

Authors:
Guosheng Hu;Liang Hu;Jing Song;Pengchao Li;Xilong Che;Hongwei Li
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Year:
2010

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Abstract

Accurate grid resources prediction is crucial for a grid scheduler In this study, support vector regression (SVR), which is an effective regression algorithm, is applied to grid resources prediction In order to build an effective SVR model, SVR's parameters must be selected carefully Therefore, we develop an ant colony optimization-based SVR (ACO-SVR) model that can automatically determine the optimal parameters of SVR with higher predictive accuracy and generalization ability simultaneously The proposed model was tested with grid resources benchmark data set Experimental results demonstrated that ACO-SVR worked better than SVR optimized by trial-and-error procedure (T-SVR) and back-propagation neural network (BPNN).