基于CNN-LSTM神经网络的磁盘故障预测方法OA北大核心CSTPCD
A DISK FAILURE PREDICTION METHOD BASED ON CNN-LSTM NEURAL NETWORK
运维人员准确预测将要发生的磁盘故障是保障数据安全的关键.然而,不平衡数据、不准确磁盘特性标记影响预测的准确性.提出一种基于预故障重置窗口(pre_Failure Reseting Window,pre_FRW)数据处理并组合卷积神经网络(CNN)和长短期记忆网络(LSTM),即pre_FRW-CNN-LSTM的磁盘故障预测方法.pre_FRW数据处理既可以解决样本不平衡,又能减少潜在的模糊样本.而CNN-LSTM模型结构能提取数据的空间特征,还能有效捕捉时间序列之间的依赖关系.在真实监控数据集上实验表明,pre_FRW-CNN-LSTM的磁盘故障预测方法对比业界其他方法提升2％～10％的故障预测率,并保持较低的错误告警率.
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.
彭福康;王恩东;高晓锋
郑州大学信息工程学院 河南郑州 450001浪潮电子信息产业股份有限公司 山东济南 250101
计算机与自动化
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
Cloud data centerPre-failure reseting windowCutting windowCNNLSTMDisk failure prediction
《计算机应用与软件》 2024 (006)
92-100,149 / 10
国家重点研发计划项目(2017YFB1001700).
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