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Large-Scale Distributed Storage for Highly Concurrent MapReduce Applications

Diana Moise 1 Gabriel Antoniu 1 Luc Bougé 1
1 KerData - Scalable Storage for Clouds and Beyond
IRISA-D1 - SYSTÈMES LARGE ÉCHELLE, Inria Rennes – Bretagne Atlantique
Abstract : A large part of today's most popular applications are data-intensive; the data volume they process is continuously growing. Specialized abstractions like Google's MapReduce and Pig-Latin were developed to efficiently manage the workloads of data-intensive applications. These models propose high-level data processing frameworks intended to hide the details of parallelization from the user. Such frameworks rely on storing huge objects and target high performance by optimizing the parallel execution of the computation. The purpose of this PhD is to provide efficient storage for the MapReduce framework and the applications it was designed for. The research conducted so far, concerned the storage layer this type of applications require. To meet these requirements we rely on BlobSeer, a system for managing massive data in a large-scale distributed context.
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https://hal.inria.fr/inria-00458143
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Submitted on : Friday, February 19, 2010 - 3:44:51 PM
Last modification on : Friday, July 10, 2020 - 4:15:16 PM
Long-term archiving on: : Thursday, October 18, 2012 - 3:30:44 PM

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Diana Moise, Gabriel Antoniu, Luc Bougé. Large-Scale Distributed Storage for Highly Concurrent MapReduce Applications. 24th IEEE International Symposium on Parallel and Distributed Processing (IPDPS 2010) - Workshop Proceedings, Apr 2010, Atlanta, United States. ⟨10.1109/IPDPSW.2010.5470806⟩. ⟨inria-00458143⟩

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