软件导刊2024，Vol.23Issue(12)：18-26,9.DOI:10.11907/rjdk.232205
基于MVMD-MHAT-BiLSTM的云资源负载预测方法
Cloud Resource Load Prediction Method Based on MVMD-MHAT-BiLSTM
摘要
Abstract
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.关键词
云资源负载预测/灰狼优化算法/变分模态分解/多头注意力机制/双向长短记忆网络/注意力机制Key words
cloud resource load prediction/GWO/VMD/multi-head attention mechanism/BiLSTM/attention mechanism分类
信息技术与安全科学引用本文复制引用
史爱武,罗干,李林逸,黄河..基于MVMD-MHAT-BiLSTM的云资源负载预测方法[J].软件导刊,2024,23(12):18-26,9.基金项目
国家自然科学基金面上项目(61170093) （61170093）
湖北省教育厅科学技术研究计划重点基金项目(D20141603) （D20141603）
