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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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