Cloud Resource Load Prediction Method Based on MVMD-MHAT-BiLSTM
Cloud computing service providers currently face huge challenges in predicting large-scale workloads and resource usage.Due to the difficulty in capturing nonlinear characteristics,traditional prediction methods usually cannot achieve high prediction performance for re-source load data.In addition,there is a lot of noise in the original time series.If smoothing algorithms are not used to denoise these time se-ries,the forecast results may not meet the provider's requirements.To this end,this paper proposes a MVMD-MHAT-BiLSTM combined pre-diction model.This model first uses the improved gray wolf optimization algorithm to optimize the VMD parameters,and then uses the varia-tional mode decomposition signal decomposition method to decompose the complex,nonlinear original The time series is decomposed into low-frequency intrinsic mode functions;then a multi-head attention mechanism is introduced in BiLSTM to capture multi-level,bidirectional fea-tures;and finally the attention mechanism is used to explore the importance of different output dimensions.Taking the CPU usage of machines in Alibaba Cloud Cluster Data as an example,compared with theBiLSTM,Pa-BiLSTM,CNN-BiLSTM,MHAT-BiLSTM and VMD-MHAT-BiLSTM,the RMSE of of the model proposed in this article decreased by 8.6%to 19.3%,achieving higher prediction accuracy.
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