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Published Articles >> Table of Contents >> Abstract
First International Conference on Autonomic Computing (ICAC'04)
pp. 44-51
File Classification in Self-* Storage Systems
Michael Mesnier, Carnegie Mellon University
Eno Thereska, Carnegie Mellon University
Gregory R. Ganger, Carnegie Mellon University
Daniel Ellard, Harvard University
Margo Seltzer, Harvard University
Full Article Text:
 
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICAC.2004.32
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| Abstract |
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To tune and manage themselves, file and storage systems must understand key properties (e.g., access pattern, lifetime, size) of their various files. This paper describes how systems can automatically learn to classify the properties of files (e.g., read-only access pattern, short-lived, small in size) and predict the properties of new files, as they are created, by exploiting the strong associations between a file’s properties and the names and attributes assigned to it. These associations exist, strongly but differently, in each of four real NFS environments studied. Decision tree classifiers can automatically identify and model such associations, providing prediction accuracies that often exceed 90%. Such predictions can be used to select storage policies (e.g., disk allocation schemes and replication factors) for individual files. Further, changes in associations can expose information about applications, helping autonomic system components distinguish growth from fundamental change.
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Citation:
Michael Mesnier, Eno Thereska, Gregory R. Ganger, Daniel Ellard, Margo Seltzer,
"File Classification in Self-* Storage Systems,"
icac,
pp. 44-51,
First International Conference on Autonomic Computing (ICAC'04),
2004
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