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SmartPred: Unsupervised Hard Disk Failure Detection

  • Due to the rapidly increasing storage consumption worldwide, as well as the expectation of continuous availability of information, the complexity of administration in today’s data centers is growing permanently. Integrated techniques for monitoring hard disks can increase the reliability of storage systems. However, these techniques often lack intelligent data analysis to perform predictiveDue to the rapidly increasing storage consumption worldwide, as well as the expectation of continuous availability of information, the complexity of administration in today’s data centers is growing permanently. Integrated techniques for monitoring hard disks can increase the reliability of storage systems. However, these techniques often lack intelligent data analysis to perform predictive maintenance. To solve this problem, machine learning algorithms can be used to detect potential failures in advance and prevent them. In this paper, an unsupervised model for predicting hard disk failures based on Isolation Forest is proposed. Consequently, a method is presented that can deal with the highly imbalanced datasets, as the experiment on the Backblaze benchmark dataset demonstrates.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/4407
Bibliografische Angaben
Title (English):SmartPred: Unsupervised Hard Disk Failure Detection
Conference:ISC High Performance 2020 International Workshops, June 21-25, 2020, Frankfurt, Germany
Author:Janis KeuperStaff MemberORCiDGND, Philipp Rombach
Year of Publication:2020
Place of publication:Cham
Publisher:Springer
Page Number:12
First Page:235
Last Page:246
Parent Title (English):High Performance Computing
Editor:Heike Jagode, Hartwig Anzt, Guido Juckeland, Hatem Ltaief
ISBN:978-3-030-59850-1 (Print)
ISBN:978-3-030-59851-8 (eBook)
DOI:https://doi.org/10.1007/978-3-030-59851-8_15
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Forschung / IMLA - Institute for Machine Learning and Analytics
Institutes:Bibliografie
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
Formale Angaben
Open Access: Closed Access 
Licence (German):License LogoUrheberrechtlich geschützt