关键词: 云计算, 灰狼优化算法, 支持向量机, 极值优化, 预测

Abstract: Cloud computing resource load short-term forecasting plays an essential role in precondition and protection of highly effective resource management, secure and stable operation of system. Due to promoting the prediction accuracy of load short-term forecasting, EGWO-SVM is proposed. Firstly, the basic principles of Grey Wolf Optimizer（GWO） are presented. Then the improved extremal optimization based GWO model is proposed. Finally, the paper builds the prototype of resource load short-term prediction model according to the optimal parameters, and tests the performance of EGWO-SVM by simulation experiments. Experimental results show that compared with the reference models, EGWO-SVM can precisely characterize the complicated trends of cloud computing resource short-term load, efficiently promote the short-term resource load prediction accuracy in cloud computing.

Key words: cloud computing, Grey Wolf Optimizer（GWO）, Support Vector Machine（SVM）, extremal optimization, prediction