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Proactive Drive Failure Prediction for Large Scale Storage Systems

机译：大规模存储系统的主动驱动器故障预测

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Most of the modern hard disk drives support Self-Monitoring, Analysis and Reporting Technology (SMART), which can monitor internal attributes of individual drives and predict impending drive failures by a thresholding method. As the prediction performance of the thresholding algorithm is disappointing, some researchers explored various statistical and machine learning methods for predicting drive failures based on SMART attributes. However, the failure detection rates of these methods are only up to 50% ~ 60% with low false alarm rates (FARs). We explore the ability of Backpropagation (BP) neural network model to predict drive failures based on SMART attributes. We also develop an improved Support Vector Machine(SVM) model. A real-world dataset concerning 23,395 drives is used to verify these models. Experimental results show that the prediction accuracy of both models is far higher than previous works. Although the SVM model achieves the lowest FAR (0.03%), the BP neural network model is considerably better in failure detection rate which is up to 95% while keeping a reasonable low FAR.
机译：大多数现代硬盘驱动器支持自监控，分析和报告技术（智能），可以通过阈值方法监控各个驱动器的内部属性并预测即将发生的驱动失败。随着阈值算法的预测性能令人失望，一些研究人员探讨了各种统计和机器学习方法，用于基于智能属性来预测驱动失败。然而，这些方法的故障检测率仅高达50％〜60％，具有低误报率（远）。我们探讨了BackProjagation（BP）神经网络模型的能力，以基于智能属性预测驱动失败。我们还开发了一种改进的支持向量机（SVM）模型。关于23,395个驱动器的真实数据集用于验证这些模型。实验结果表明，两种型号的预测精度远远高于以前的作品。虽然SVM模型实现了最低（0.03％），但BP神经网络模型的故障检测率明显好于95％，同时保持合理低。

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