@inproceedings{KeuperRombach2020,
  author    = {Janis Keuper and Philipp Rombach},
  title     = {SmartPred: Unsupervised Hard Disk Failure Detection},
  series = {High Performance Computing},
  editor    = {Heike Jagode and Hartwig Anzt and Guido Juckeland and Hatem Ltaief},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-59850-1 (Print)},
  doi       = {10.1007/978-3-030-59851-8\_15},
  pages     = {235 -- 246},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}