Grid Resource Prediction based on Support Vector Regression and Simulated Annealing Algorithms

Abstract

Accurate grid resources prediction is crucial for a grid scheduler. In this study, support vector regression (SVR), which is a novel and 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 a simulated annealing algorithm-based SVR (SA-SVR) model that can automatically determine the optimal parameters of SVR with higher predictive accuracy and generalization ability simultaneously. The performance of the hybrid model (SA-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 SA-SVR model works better than the other two models.