Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application
The Journal of Machine Learning Research
Disk failures in the real world: what does an MTTF of 1,000,000 hours mean to you?
FAST '07 Proceedings of the 5th USENIX conference on File and Storage Technologies
Failure trends in a large disk drive population
FAST '07 Proceedings of the 5th USENIX conference on File and Storage Technologies
A reliability optimization method for RAID-structured storage systems based on active data migration
Journal of Systems and Software
Early security classification of skype users via machine learning
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
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This paper presents a detector of soon-to-fail disks based on a combination of statistical models. During operation the detector takes as input a performance signal from each disk and sends and alarm when there is enough evidence (according to the models) that the disk is not healthy. The parameters of these models are automatically trained using signals from healthy and failed disks. In an evaluation on a population of 1190 production disks from a popular customer-facing internet service, the detector was able to predict 15 out of the 17 failed disks (88:2% detection) with 30 false alarms (2:56% false positive rate).