PDL PROJECTS

ATTRIBUTE-BASED LEARNING ENVIRONMENTS (ABLE)

Contact: Greg Ganger


Models, created from file attributes, are used to classify the properties of existing files and predict the properties of new files when they are created.

Overview

To tune and manage themselves, file and storage systems must understand key properties (e.g., access pattern, lifetime, popularity) of their files. ABLE allows systems to learn how to automatically classify files 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. Such predictions can be used to select policies (e.g., disk allocation schemes and replication factors) for individual files. Further, changes in associations can expose information about applications, helping self-* system components distinguish growth from fundamental change. For further information, see the extended overview.

People

FACULTY

Greg Ganger

STUDENTS

Mike Mesnier
Eno Thereska

Publications

Acknowledgements

We thank the members and companies of the PDL Consortium: Actifio, American Power Conversion, EMC Corporation, Facebook, Fusion-io,Google, Hewlett-Packard Labs, Hitachi, Huawei Technologies Co., Intel Corporation, Microsoft Research, NEC Laboratories, NetApp, Inc., Oracle Corporation, Panasas, Samsung Information Systems America, Seagate Technology, Symantec Corporation, VMware, Inc., and Western Digital for their interest, insights, feedback, and support.

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© 2014. Last updated 7 March, 2012