Affiliations: [a] Xi’an Technological University, School of Computer Science and Engineering, Xi’an, China
| [b] Beijing Aerospace Automatic Control Institution, Beijing, China
Correspondence:
[*]
Corresponding author. Zhaofeng Pan, Xi’an Technological University, School of Computer Science and Engineering, Xi’an, China. Tel.: +86 13060458859; E-mail: panzhaofeng@st.xatu.edu.cn.
Abstract: Hard disk is the main storage device for cloud service, and there always contain massive disks deployed in a data center. Disk failure occur frequently in data center, which may lead to data loss and other disasters, so there have urgent needs for a failure prediction method of hard disk so as to ensure service reliability. This paper proposes a temporal prediction model based on LSTM. Firstly, the SMART data of the disk is analyzed, and the Pearson correlation coefficient is used to analyze the correlation between the monitoring time series data of the faulty disk and the normal disk, and the monitoring index with the lowest correlation is selected as the fault feature; secondly, for the problem of serious imbalance of positive and negative samples in the SMART dataset, the SMOTEENN algorithm is introduced for oversampling to obtain a balanced dataset of positive and negative samples. The proposed method improves accuracy by 8.268% and F1-score by 8.657% compared to the conventional method.
Keywords: Hard disk drives, failure
prediction, association analysis, long-short term memory, SSA
