Hard Disk Drive Failure Prediction for Mobile Edge Computing Based on an LSTM Recurrent Neural Network

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.

Automated summary

With the advancement of technology in daily life applications such as real-time video surveillance systems, mobile edge computing, and Internet of things (IoT), the reliability of these systems can be greatly affected due to hard disk drive (HDD) failures. This paper proposes an LSTM recurrent neural network-based HDD failure prediction model and a new health degree evaluation method, which demonstrates good prediction accuracy with low overhead in comprehensive experiments on two real-world hard drive datasets.