计算机应用与软件2024，Vol.41Issue(6)：92-100,149,10.DOI:10.3969/j.issn.1000-386x.2024.06.014
基于CNN-LSTM神经网络的磁盘故障预测方法
A DISK FAILURE PREDICTION METHOD BASED ON CNN-LSTM NEURAL NETWORK
摘要
Abstract
Accurate prediction from operation and maintenance personnel of the upcoming disk failure is the key to ensure data security.However,unbalanced data and inaccurate disk characteristic marking affect the accuracy of prediction.This paper proposes a disk failure prediction method based on pre_Failure Reseting Window(pre_FRW)data processing and combining convolutional neural network(CNN)and long short-term memory network(LSTM),namely pre_FRW-CNN-LSTM.The pre_FRW data processing could not only solve the sample imbalance,but also reduce the potential fuzzy samples.The CNN-LSTM model structure could extract the spatial characteristics of the data,and it could also effectively capture the dependencies between time series.Experiments on real monitoring data sets show that the disk failure prediction method of pre_FRW-CNN-LSTM improves the failure prediction rate by 2％-10％compared with other methods in the industry and maintains a low false alarm rate.关键词
云数据中心/预故障重置窗口/截断窗口/卷积神经网络/长短期记忆网络/磁盘故障预测Key words
Cloud data center/Pre-failure reseting window/Cutting window/CNN/LSTM/Disk failure prediction分类
信息技术与安全科学引用本文复制引用
彭福康,王恩东,高晓锋..基于CNN-LSTM神经网络的磁盘故障预测方法[J].计算机应用与软件,2024,41(6):92-100,149,10.基金项目
国家重点研发计划项目(2017YFB1001700). （2017YFB1001700）
