Support Vector Regression and Ant Colony Optimization for Grid Resources Prediction

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).