Hard Drive Failure Prediction Using Classification and Regression Trees

Li, Jing; Ji, Xinpu; Jia, Yunfeng; Zhu, Bingpeng; Wang, Gang; Li, Zhongwei; Liu, Xiaoguang

doi:10.1109/dsn.2014.44

Cited by 120 publications

(44 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Health. Drive health evaluation influences the prediction accuracy of soon-to-fail drives directly and primarily falls into three groups: the binary method [8,20], phase method [17], and health degree method [11,18,21]. e binary method categorizes drive health into two states: failed and good.…”

Section: Evaluation Of Drivementioning

confidence: 99%

“…To improve the performance of HDD failures prediction, many machine-learning-based prediction approaches have been proposed, including Bayesian algorithms [6][7][8][9], support vector machine (SVM) [10], classification tree (CT) [11,12], random forest (RF) [13,14], artificial neural network (ANN) [15], convolution neural network (CNN) [16], and recurrent neural network (RNN) [17,18]. RNN-based prediction models achieve the highest FDRs, and RF-based models attain the lowest FARs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Shen

Ren

Wan

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

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.

show abstract

Section: Evaluation Of Drivementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Shen

Ren

Wan

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…These algorithms are not highly employed because they require a high level of care and only provide 40% accuracy [Hughes, Murray, Kreutz et al (2002)]. In Li et al [Li, Ji, Jia et al (2014)], a novel HDDs failure prediction model is implemented. Their methodology is based on regression trees and classification trees which is robust in the prediction performance, interpretability and stability while comparing with state-of-theart models.…”

Section: Smart (Self-monitoring Analysis and Reporting Technology)mentioning

confidence: 99%

“…If more than N/2 voters have predicted the negative result, then HDD is classified to be failed. This concept of N/2 voters is derived from Li et al [Li, Ji, Jia et al (2014)]. When the HDD is predicted to be failed, its lead time (Time Before Failure) is predicted and health degree is examined.…”

Section: Introductionmentioning

confidence: 99%

Failure Prediction, Lead Time Estimation and Health Degree Assessment for Hard Disk Drives Using Voting based Decision Trees

Kaur¹,

Kaur²

2019

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Hard Disk drives (HDDs) are an essential component of cloud computing and big data, responsible for storing humongous volumes of collected data. However, HDD failures pose a huge challenge to big data servers and cloud service providers. Every year, about 10% disk drives used in servers crash at least twice, lead to data loss, recovery cost and lower reliability. Recently, the researchers have used SMART parameters to develop various prediction techniques, however, these methods need to be improved for reliability and real-world usage due to the following factors: they lack the ability to consider the gradual change/deterioration of HDDs; they have failed to handle data unbalancing and biases problem; they don't have adequate mechanisms for health status prediction of HDDs. This paper introduces a novel voting-based decision tree classifier to cater failure prediction, a balance splitting algorithm for the data unbalancing problem, an advanced procedure for lead time estimation and R-CNN based approach for health status estimation. Our system works robustly by considering a gradual change in SMART parameters. The system is rigorously tested on 3 datasets and it delivered benchmarks results as compared to the state of the art.

show abstract

“…No obstante, las técnicas que permiten la realización de pronósticos se utilizan en un sinnúmero de distintas actividades del quehacer y el conocimiento [1][2][3]. Existe un alto número de técnicas y herramientas susceptibles de ser aplicadas, algunas de carácter específico y otras con un valor general [4][5][6].…”

Section: Introductionunclassified

Predicción de resultados académicos de estudiantes de informática mediante el uso de redes neuronales

Blanco

Fernández

Lovelle

et al. 2016

Ingeniare. Rev. chil. ing.

View full text Add to dashboard Cite

show abstract

Hard Drive Failure Prediction Using Classification and Regression Trees

Cited by 120 publications

References 10 publications

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

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

Failure Prediction, Lead Time Estimation and Health Degree Assessment for Hard Disk Drives Using Voting based Decision Trees

Predicción de resultados académicos de estudiantes de informática mediante el uso de redes neuronales

Contact Info

Product

Resources

About