非分布环境下硬盘故障预测的启发式irm方法

Jichao Wang, Ran Zhang, Guanqiang Qi, Lanqing Hong
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引用次数: 2

摘要

硬盘驱动器(HDD)是位于各种信息基础设施的主要层中的基本设备。长期磁盘故障预测对于数据中心存储系统的稳定性和健壮性至关重要。本文提出了一种领域自适应方法来提高非分布磁盘数据集的预测性能。我们提出了一种带有新的损失函数的启发式不变风险最小化(HIRM)方法来处理不平衡数据。验证了HIRM与机器学习模型相结合,提高了对超分布(OoD)数据的准确性和稳定性。当数据中心中引入具有新的SMART特征分布的硬盘时,所提出的HIRM算法比普通神经网络获得了更好的结果。以BackBlaze数据中心的数据为例,说明了HIRM模型的应用。每个人的目标都不一样。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Heuristic-IRM Method on Hard Disk Failure Prediction in Out-of-distribution Environments
The hard disk drives (HDD) are essential devices lying in primary layers of diverse information infrastructure. Long-term disk failure predictions are crucial to the stability and robustness of storage systems for data centers. In this paper, a domain adaption method is developed to improve prediction performance in out-of-distribution disk datasets. We propose heuristic invariant risk minimization (HIRM) with a new loss function to deal with imbalanced data. The HIRM combined with machine learning models are verified to promote the accuracy and stability in out-of-distribution (OoD) data. When hard disks with new SMART feature distribution are introduced into the data center, the proposed HIRM algorithm achieves better results than vanilla neural networks. A numerical example using the data from the BackBlaze data center is shown to illustrate the application of our HIRM model. The aims of each person are different.
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