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Master Thesis

Immediate data management for map/reduce applications

Abstract : Map/Reduce is a popular programming model and an associated implementation for processing large data sets nowadays. This report aims to present the problem of managing intermediate data which is generated during Map/Reduce computations. We focus on the Hadoop Map/Reduce framework and two file systems, Hadoop Distributed File System and BlobSeer File System, used as storage backends of Hadoop Map/Reduce framework, which substitute for the original intermediate storage layer. As a result, our new design deals with data reliability and efficiency, thanks to BlobSeer which supports access concurrency. The prototype has been experimented on the Grid'5000 testbed, using up to 150 nodes.
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Contributor : Co-Responsables Du Mri V. Gouranton Et S. Blazy <>
Submitted on : Friday, October 29, 2010 - 7:48:27 PM
Last modification on : Monday, February 15, 2021 - 10:37:19 AM
Long-term archiving on: : Sunday, January 30, 2011 - 3:08:26 AM


  • HAL Id : dumas-00530784, version 1


Thi-Thu-Lan Trieu. Immediate data management for map/reduce applications. Calcul parallèle, distribué et partagé [cs.DC]. 2010. ⟨dumas-00530784⟩



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