Carnegie Mellon University
Browse
- No file added yet -

File Classification in Self-* Storage Systems (CMU-PDL-04-101)

journal contribution
posted on 2004-01-01, 00:00 authored by Michael Mesnier, Eno Thereska, Daniel Ellard, Gregory R. Ganger, Margo Seltzer
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.

History

Publisher Statement

All Rights Reserved

Date

2004-01-01

Usage metrics

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC