面向磁盘故障预测的机器学习方法比较

磁盘是保存数据的重要载体,提高磁盘的可靠性和数据可用性具有重要意义。现代磁盘普遍支持SMART协议,用来监控磁盘的内部工作状态。采用机器学习方法,分析磁盘的SMART信息,实现对磁盘故障的预测。所采用的机器学习方法包括反向神经网络、决策树、支持向量机以及简单贝叶斯,并采用实际磁盘SMART数据进行验证与分析。基于上述数据,对不同机器学习方法的有效性及其效果进行了对比。结果表明,决策树方法的预测率最好,支持向量机方法的误报率最低。

Comparison of machine learning methods for disk failure prediction

As disk is one of the most important data storage device, it is significant to improve disks’ reliability and data availability. Modern disks adopt the SMART protocol to monitor the internal operating status. We employ machine learning methods, including back propagation neural networks, decision tree, supported vector machine and nave Bayes to analyze the SMART data of disks, which can predict disk failures. Real SMART data of disks are used in experiments to validate and analyze the effectiveness of those methods, and the effectiveness of different methods is compared. The results show that the decision tree method has best prediction rate while the supported vector machine method has the lowest false alarm rate.