NDLI: Automatic generation of behavioral hard disk drive access time models

Content Provider	IEEE Xplore Digital Library
Author	Crume, A. Maltzahn, C. Ward, L. Kroeger, T. Curry, M.
Copyright Year	2014
Description	Author affiliation: Univ. of California, Santa Cruz, Santa Cruz, CA, USA (Crume, A.; Maltzahn, C.) \|\| Sandia Nat. Labs., Albuquerque, NM, USA (Ward, L.; Kroeger, T.; Curry, M.)
Abstract	Predicting access times is a crucial part of predicting hard disk drive performance. Existing approaches use white-box modeling and require intimate knowledge of the internal layout of the drive, which can take months to extract. Automatically learning this behavior is a much more desirable approach, requiring less expert knowledge, fewer assumptions, and less time. While previous research has created black-box models of hard disk drive performance, none have shown low per-request errors. A barrier to machine learning of access times has been the existence of periodic behavior with high, unknown frequencies. We identify these high frequencies with Fourier analysis and include them explicitly as input to the model. In this paper we focus on the simulation of access times for random read workloads within a single zone. We are able to automatically generate and tune request-level access time models with mean absolute error less than 0.15 ms. To our knowledge this is the first time such a fidelity has been achieved with modern disk drives using machine learning. We are confident that our approach forms the core for automatic generation of access time models that include other workloads and span across entire disk drives, but more work remains.
Starting Page	1
Ending Page	11
File Size	5307702
Page Count	11
File Format	PDF
ISBN	9781479956715
DOI	10.1109/MSST.2014.6855553
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-02
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hard disks Neural networks Fourier transforms Data models Decision trees Genetic algorithms Time-frequency analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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