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DHISC : Disk Health Indexing System for Centers of Data Management

Author(s)
Kekelishvili, Rebecca.
Thumbnail
Download1017566745-MIT.pdf (15.67Mb)
Alternative title
Disk Health Indexing System for Centers of data management
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Katrina LaCurtis and Wayne Booth.
Terms of use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
If we want to have reliable data centers, we must improve reliability at the lowest level of data storage at the disk level. To improve reliability, we need to convert storage systems from reactive mechanisms that handle disk failures to a proactive mechanism that predict and address failures. Because the definition of disk failure is specific to a customer rather than defined by a standard, we developed a relative disk health metric and proposed a customer-oriented disk-maintenance pipeline. We designed a program that processes data collected from data center disks into a format that is easy to analyze using machine learning. Then, we used a neural network to recognize disks that show signs of oncoming failure with 95.4-98.7% accuracy, and used the result of the network to produce a rank of most and least reliable disks at the data center, enabling customers to perform bulk disk maintenance, decreasing system downtime.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (pages 85-88).
 
Date issued
2017
URI
http://hdl.handle.net/1721.1/113181
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

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