With the increase in intelligence applications and services, like real-time video surveillance systems, mobile edge computing, and Internet of things (IoT), technology is greatly involved in our daily life. However, the reliability of these systems cannot be always guaranteed due to the hard disk drive (HDD) failures of edge nodes. Specifically, a lot of read/write operations and hazard edge environments make the maintenance work even harder. HDD failure prediction is one of the scalable and low-overhead proactive fault tolerant approaches to improve device reliability. In this paper, we propose an LSTM recurrent neural network-based HDD failure prediction model, which leverages the long temporal dependence feature of the drive health data to improve prediction efficiency. In addition, we design a new health degree evaluation method, which stores current health details and deterioration. The comprehensive experiments on two real-world hard drive datasets demonstrate that the proposed approach achieves a good prediction accuracy with low overhead.

中文翻译：

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基于LSTM递归神经网络的移动边缘计算硬盘驱动器故障预测

随着诸如实时视频监视系统，移动边缘计算和物联网（IoT）之类的智能应用程序和服务的增加，技术已极大地介入了我们的日常生活。但是，由于边缘节点的硬盘驱动器（HDD）故障，不能始终保证这些系统的可靠性。特别是，许多读/写操作和危险边缘环境使维护工作变得更加困难。HDD故障预测是可扩展且开销较低的主动容错方法之一，可以提高设备的可靠性。在本文中，我们提出了一种基于LSTM递归神经网络的HDD故障预测模型，该模型利用了驱动器健康数据的长期时间依赖性特性来提高预测效率。此外，我们设计了一种新的健康度评估方法，它存储当前的健康详细信息和恶化情况。在两个真实世界的硬盘数据集上进行的综合实验表明，该方法可实现较高的预测精度，且开销较低。