TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Keuper, Janis
A1  - Rombach, Philipp
ED  - Jagode, Heike
ED  - Anzt, Hartwig
ED  - Juckeland, Guido
ED  - Ltaief, Hatem
T1  - SmartPred: Unsupervised Hard Disk Failure Detection
T2  - High Performance Computing
N2  - 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 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.
Y1  - 2020
SN  - 978-3-030-59850-1 (Print)
SB  - 978-3-030-59850-1 (Print)
SN  - 978-3-030-59851-8 (eBook)
SB  - 978-3-030-59851-8 (eBook)
U6  - https://doi.org/10.1007/978-3-030-59851-8_15
DO  - https://doi.org/10.1007/978-3-030-59851-8_15
SP  - 235
EP  - 246
S1  - 12
PB  - Springer
CY  - Cham
ER  -