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Deepavali Bhagwat
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Deepavali Bhagwat is a PhD candidate working with Prof. Darrell Long in the Archival Storage project. Her research focuses on scalability issues with respect to data de-duplication, indexing and retrieval for archival and backup storage systems. She is also interesting in developing solutions that apply the right level of redundancy to de-duplicated data for high availability. Along with her advisor she also works with researchers at Hewlett Packard Laboratories, Palo Alto.
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Publications
2009
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Deepavali Bhagwat,
Kave Eshghi,
Darrell D. E. Long,
Mark Lillibridge,
Extreme Binning: Scalable, Parallel Deduplication for Chunk-based File Backup ,
Proceedings of the 17th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2009),
September 2009.
[Deduplication]
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Mark Lillibridge,
Kave Eshghi,
Deepavali Bhagwat,
Vinay Deolalikar,
Greg Trezise,
Peter Camble,
Sparse Indexing: Large Scale, Inline Deduplication Using Sampling and Locality,
Proceedings of the 7th USENIX Conference on File and Storage Technologies (FAST '09),
February 2009.
[Deduplication]
2007
2006
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Deepavali Bhagwat,
Kristal Pollack,
Darrell D. E. Long,
Thomas Schwarz,
Ethan L. Miller,
Jehan-François Pâris,
Providing High Reliability in a Minimum Redundancy Archival Storage System,
Proceedings of the 14th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS '06),
September 2006, pages 413-421.
[Archival Storage]
[Deduplication]
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Last modified 16 Sep 2008
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