![]() In this work, we introduce methods of extracting meaningful attributes associated with operational failure and of pre-processing the highly imbalanced health statistics data for subsequent prediction tasks using data-driven approaches. ![]() In this regard, early detection of impending failure at the disk level aids in reducing system downtime and reduces operational loss making proactive health monitoring a priority for AIOps in such settings. Recent observations point to hard disk reliability as one of the most pressing reliability issues in data centers containing massive volumes of storage devices such as HDDs. The comprehensive experiments on two real-world hard drive datasets demonstrate that the proposed approach achieves a good prediction accuracy with low overhead.Ībstract: Physical and cloud storage services are well-served by functioning and reliable high-volume storage systems. In addition, we design a new health degree evaluation method, which stores current health details and deterioration. 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. HDD failure prediction is one of the scalable and low-overhead proactive fault tolerant approaches to improve device reliability. Specifically, a lot of read/write operations and hazard edge environments make the maintenance work even harder. ![]() However, the reliability of these systems cannot be always guaranteed due to the hard disk drive (HDD) failures of edge nodes. as mentioned in this paper proposed 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.Ībstract: 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. ![]()
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