应用多变量时间序列模型用于高性能计算 (HPC) 的故障预测.
Xiangdong Pei1,2, Min Yuan2, Guo Mao1
1College of Computer, National University of Defense Technology, Changsha, China.
这项研究介绍了超级计算机系统的新型故障预测模型. 通过使用深度学习,CBA-net模型通过准确预测故障发生和位置来提高可靠性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 高性能计算 高性能计算
背景情况:
- 由于其规模和复杂性,超级计算系统面临着越来越高的可靠性要求.
- 有效的故障预测对于保持这些系统的运行完整性至关重要.
- 现有的方法难以捕捉复杂的时空错误模式.
研究的目的:
- 为大型超级计算机系统提出一个多维的融合故障预测模型.
- 在复杂的计算环境中提高故障预测的准确性和效率.
- 解决先进计算基础设施中高可靠性的需求.
主要方法:
- 开发了一种新的CBA-net (CNN-BiLSTAM-Attention) 模型,其中包含HDBSCAN集群用于数据预处理.
- 利用深度学习从故障日志中提取和学习空间和时间特征.
- 专注于对时间序列特征的敏感性和局部特征提取.
主要成果:
- 实现了0.031的根平均平方误差 (RMSE) 对于故障发生时间预测.
- 对于故障发生节点位置,实现了93%的平均预测准确度.
- 展示了快速的融合和改进的细粒度故障预测能力.
结论:
- 拟议的CBA-net模型有效地预测大型超级计算机的故障.
- 该模型提取时空特征的能力导致了高预测准确度.
- 这种方法显著提高了超级计算机系统的可靠性和细粒度故障预测.
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