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Application of multivariate time-series model for high performance computing (HPC) fault prediction

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  • Xiangdong Pei
  • Min Yuan
  • Guo Mao
  • Zhengbin Pang

Abstract

Aiming at the high reliability demand of increasingly large and complex supercomputing systems, this paper proposes a multidimensional fusion CBA-net (CNN-BiLSTAM-Attention) fault prediction model based on HDBSCAN clustering preprocessing classification data, which can effectively extract and learn the spatial and temporal features in the predecessor fault log. The model can effectively extract and learn the spatial and temporal features from the predecessor fault logs, and has the advantages of high sensitivity to time series features and sufficient extraction of local features, etc. The RMSE of the model for fault occurrence time prediction is 0.031, and the prediction accuracy of node location for fault occurrence is 93% on average, as demonstrated by experiments. The model can achieve fast convergence and improve the fine-grained and accurate fault prediction of large supercomputers.

Suggested Citation

  • Xiangdong Pei & Min Yuan & Guo Mao & Zhengbin Pang, 2023. "Application of multivariate time-series model for high performance computing (HPC) fault prediction," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-18, October.
    • Handle: RePEc:plo:pone00:0281519
    DOI: 10.1371/journal.pone.0281519
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    1. Mandakini Behera & Archana Sarangi & Debahuti Mishra & Pradeep Kumar Mallick & Jana Shafi & Parvathaneni Naga Srinivasu & Muhammad Fazal Ijaz, 2022. "Automatic Data Clustering by Hybrid Enhanced Firefly and Particle Swarm Optimization Algorithms," Mathematics, MDPI, vol. 10(19), pages 1-29, September.
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