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Published Articles >> Table of Contents >> Abstract
12th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS'04)
pp. 588-595
Storage Device Performance Prediction with CART Models
Mengzhi Wang, Carnegie Mellon University
Kinman Au, Carnegie Mellon University
Anastassia Ailamaki, Carnegie Mellon University
Anthony Brockwell, Carnegie Mellon University
Christos Faloutsos, Carnegie Mellon University
Gregory R. Ganger, Carnegie Mellon University
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MASCOT.2004.1348316
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| Abstract |
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Storage device performance prediction is a key element
of self-managed storage systems. This work explores the application
of a machine learning tool, CART models, to storage
device modeling. Our approach predicts a device’s performance
as a function of input workloads, requiring no
knowledge of the device internals. We propose two uses of
CART models: one that predicts per-request response times
(and then derives aggregate values) and one that predicts
aggregate values directly from workload characteristics. After
being trained on the device in question, both provide accurate
black-box models across a range of test traces from
real environments. Experiments show that these models predict
the average and 90th percentile response time with a
relative error as low as 19%, when the training workloads
are similar to the testing workloads, and interpolate well
across different workloads.
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 Additional Information
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Citation:
Mengzhi Wang, Kinman Au, Anastassia Ailamaki, Anthony Brockwell, Christos Faloutsos, Gregory R. Ganger,
"Storage Device Performance Prediction with CART Models,"
mascots,
pp. 588-595,
12th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems (MASCOTS'04),
2004
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