(Enter summary)
Abstract: Storage device performance prediction is a key element of self-managed storage systems and application planning tasks, such as
data assignment. 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 ... (Update)
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BibTeX entry: (Update)
Mengzhi Wang, Kinman Au, Anastassia Ailamaki, Anthony Brockwell, Christos Faloutsos, and Gregory R. Ganger. Storage device performance prediction with CART models. SIGMETRICS Perform. Eval. Rev., 32(1):412--413, 2004. http://citeseer.ist.psu.edu/wang04storage.html More
@misc{ wang04storage,
author = "M. Wang and K. Au and A. Ailamaki and A. Brockwell and C. Faloutsos and
G. Ganger",
title = "Storage device performance prediction with CART models",
text = "Mengzhi Wang, Kinman Au, Anastassia Ailamaki, Anthony Brockwell, Christos
Faloutsos, and Gregory R. Ganger. Storage device performance prediction
with CART models. SIGMETRICS Perform. Eval. Rev., 32(1):412--413, 2004.",
year = "2004",
url = "citeseer.ist.psu.edu/wang04storage.html" }
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