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Medium Data on Big Data Predicting Disk Failures in CERNs NetApp-based Data Storage System
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

I describe in this report an experimental system for using classification and regression trees to generate predictions of disk failures in a NetApp-based storage system at the European Organisation for Nuclear Research (CERN) based on a mixture of SMART data, system logs, and low-level system performance dataparticular to NetApp's storage solutions. Additionally, I make an attempt at profiling the system's built-in failure prediction method, and compiling statistics on historical complete-disk failures as well as bad blocks developed. Finally, I experiment with various parameters for producing classification trees and end up with two candidate models which have a true-positive rate of 86% with a false-alarm rate of 4% or atrue-positive rate of 71% and a false-alarm rate of 0.9% respectively, illustrating that classification trees might be a viable method for predicting real-life disk failures in CERNs storage systems.

Place, publisher, year, edition, pages
2017. , p. 57
Series
IT ; 17081
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-337638OAI: oai:DiVA.org:uu-337638DiVA, id: diva2:1170433
Educational program
Bachelor Programme in Computer Science
Supervisors
Examiners
Available from: 2018-01-17 Created: 2018-01-03 Last updated: 2018-01-17Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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