智能系统学报2025，Vol.20Issue(2)：389-399,11.DOI:10.11992/tis.202310043
基于ECA-TCN的数据中心磁盘故障预测
Disk failure prediction in data centers based on ECA-TCN
摘要
Abstract
With the continuous expansion of the scale of the data center,disk failure has an increasing impact on the sta-bility of the data center.Current prediction methods still have shortcomings in the face of large-scale,high-dimensional and long sequence of disk running data.This paper proposes an efficient channel attention-temporal convolutional net-work(ECA-TCN)model.By combining the advantages of one-dimensional convolution of traditional convolutional neural network,integrating dilated convolution and residual structure,and introducing attention mechanism,the model can improve the accuracy and stability of disk failure prediction.In the experiment,the ECA-TCN model is compared with other classical deep learning methods.The experimental results show that the ECA-TCN model has high accuracy and stability in the disk failure prediction task.关键词
磁盘故障预测/长短时记忆网络/循环神经网络/扩张卷积/高效通道注意力机制/神经网络模型/时间序列预测/深度学习优化Key words
disk failure prediction/long short-term memory network/recurrent neural network/dilated convolution/effi-cient channel attention mechanism/neural network model/time series prediction/deep learning optimization分类
信息技术与安全科学引用本文复制引用
张铭泉,王宝兴..基于ECA-TCN的数据中心磁盘故障预测[J].智能系统学报,2025,20(2):389-399,11.基金项目
中央高校基本科研业务费专项项目(2020MS122). （2020MS122）
