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In the clustered system failure prediction, the long-time series prediction was accompanied by problem such as gradient disappearance or explosion, due to the loss of key feature information, which would affect the accuracy of the model for failure prediction. For this reason, a new model of cluster system fault prediction method based on deep learning was proposed. The method adopted bidirectional gate recurrent unit(BiGRU) to capture local timing features while employing Transformer to improve the global feature extraction capability. The dynamic changes of timing features on the cluster system logs were obtained through bidirectional information transfer in the BiGRU layer to capture the potential causality and local temporal features in the cluster events.The Transformer layer was used to process the time series output from the BiGRU layer in parallel to obtain the global temporal dependence, which followed by the fully connected neural network layer to obtain the prediction results. The effectiveness of the method was validated on a public dataset constructed from real logs generated by the Blue Gene/L system. The results showed that the proposed method outperformed the comparison methods with a best-correct rate and F1 value of 91.69% and 92.74%, respectively.
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Basic Information:
DOI：10.13705/j.issn.1671-6841.2023021
China Classification Code:TP277;TP18
Citation Information:
[1]JI Lixia,ZHANG Qingkai,ZHOU Hongxin ,et al.A Cluster System Failure Prediction Approach Based on Deep Learning[J].Journal of Zhengzhou University(Natural Science Edition),2024,56(05):71-79.DOI:10.13705/j.issn.1671-6841.2023021.
Fund Information:
国家自然科学基金项目(52179144); 河南省重大科技专项(201300210500); 郑州市重大科技创新专项(2020CXZX0053)
2023-12-16
2023-12-16
2023-12-16