南京信息工程大学学报2023，Vol.15Issue(6)：652-661,10.DOI:10.13878/j.cnki.jnuist.20230108001
基于组合注意力模型EAAT的云KPI数据预测方法
Cloud KPI data prediction based on combined attention model EAAT
摘要
Abstract
To accurately analyze the dynamics and changing trends of KPI(Key Performance Indicator)data in the daily monitoring of cloud computing clusters and predict its subsequent development to achieve high availability of cloud computing clusters,we propose a three-frequency cloud KPI data prediction approach based on combined attention model of EWT-ARIMA-Auto-TPA(EAAT for short).First,low,medium and high frequency Intrinsic Mode Variables(IMFs)of cloud KPI data are obtained via Empirical Wavelet Transform(EWT)to reduce the complexi-ty of data prediction.Second,according to the information characteristics of low,medium and high frequency IMFs obtained from the decomposition,models of ARIMA,Autoformer,and TPA-BiLSTM are used to predict each type of IMFs.Finally,the classification prediction results are combined through the Inverse EWT(IEWT)to obtain the pre-diction result of the KPI.The proposed prediction approach has been verified on four datasets from Google and Ama-zon.Whether the data is periodic and stable or not,the proposed approach outperforms comparison models.关键词
云KPI数据/时序预测方法/经验小波变换/组合注意力模型/双向长短时记忆网络Key words
cloud KPI data/time series prediction/empirical wavelet transform(EWT)/combined attention model/bidirectional long short-term memory(BiLTSM)network分类
信息技术与安全科学引用本文复制引用
丁建立,龚子恒..基于组合注意力模型EAAT的云KPI数据预测方法[J].南京信息工程大学学报,2023,15(6):652-661,10.基金项目
国家自然科学基金民航联合基金项目(U2033205,U1833114) （U2033205,U1833114）
