Workload prediction of cloud computing based on SVM and BP neural networks

Abstract

In this study, support vector machine (SVM) and back-propagation (BP) neural networks were combined to predict the workload of cloud computing physical machine, so as to improve the work efficiency of physical machine and service quality of cloud computing. Then, the SVM and BP neural network was simulated and analyzed in MATLAB software and compared with SVM, BP and radial basis function (RBF) prediction models. The results showed that the average error of the SVM and BP based model was 0.670%, and the average error of SVM, BP and RBF was 0.781%, 0.759% and 0.708%, respectively; in the multi-step prediction, the prediction accuracy of SVM, BP, RBF and SVM + BP in the first step was 89.3%, 94.6%, 96.3% and 98.5%, respectively, the second step was 87.4%, 93.1%, 95.2% and 97.8%, respectively, the third step was 83.5%, 90.3%, 93.1% and 95.7%, the fourth step was 79.1%, 87.4%, 90.5% and 93.2%, respectively, the fifth step was 75.3%, 81.3%, 85.9% and 91.1% respectively, and the sixth step was 71.1%, 76.6%, 82.1% and 89.4%, respectively.