关键词: 云计算资源负载预测, 正余混沌双弦鲸鱼优化算法, 小波包变换, 多层感知器神经网络

Abstract: In order to solve the problem caused by the traditional load forecasting of cloud computing resources method’s low prediction accuracy of sequence high frequency components and weak generalization ability, this paper proposes a method of combining wavelet packet decomposition and Whale Optimization Algorithm based on Chaotic Sine Cosine operator（CSCWOA） to optimize the MultiLayer Perceptron（MLP） neural network for short-term cloud computing resources forecasting. Load sequence is decomposed into multiple band series by wavelet packet. While the CSCWOA-MLP optimized neural network is enrolled to predict the sub-band series. And by superimposing and reconstructing all the forecasting series, it gets the actual forecasting results. The experimental results show that the method can grasp the change rule of the impact burr perfectly, which has good prediction accuracy and generalization ability.

Key words: load forecasting of cloud computing resources, whale optimization algorithm based on chaotic sine cosine operator, wavelet packet transform, multilayer perceptron neural network