| Domaine : |
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Informatique/Calcul parallèle, distribué et partagé
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| Titre : |
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Towards Scalable Data Management for Map-Reduce-based Data-Intensive Applications on Cloud and Hybrid Infrastructures |
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| Auteur(s) : |
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Gabriel Antoniu ( ,  )1, Julien Bigot ()2, Christophe Blanchet3, Luc Bougé ()1, François Briant4, Franck Cappello5, 6, 7, Alexandru Costan ()1, Frédéric Desprez ()2, Gilles Fedak ()2, Sylvain Gault2, Kate Keahey ()8, Bogdan Nicolae ()5, 6, Christian Pérez ()2, Anthony Simonet2, Frédéric Suter ()9, Bing Tang2, Raphael Terreux3 |
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| Projet(s) / laboratoire(s) : |
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| Résumé : |
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As Map-Reduce emerges as a leading programming paradigm for data-intensive computing, today's frameworks which support it still have substantial shortcomings that limit its potential scalability. In this paper we discuss several directions where there is room for such progress: they concern storage efficiency under massive data access concurrency, scheduling, volatility and fault-tolerance. We place our discussion in the perspective of the current evolution towards an increasing integration of large-scale distributed platforms (clouds, cloud federations, enterprise desktop grids, etc.). We propose an approach which aims to overcome the current limitations of existing Map-Reduce frameworks, in order to achieve scalable, concurrency-optimized, fault-tolerant Map-Reduce data processing on hybrid infrastructures. This approach will be evaluated with real-life bio-informatics applications on existing Nimbus-powered cloud testbeds interconnected with desktop grids. |
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| Langue du document : |
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Anglais |
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| Type de publication : |
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Communications avec actes |
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| Date de publication : |
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2012 |
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| Audience : |
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internationale |
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| Titre conférence : |
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1st International IBM Cloud Academy Conference - ICA CON 2012 |
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| Ville : |
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Research Triangle Park, North Carolina |
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| Pays : |
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États-Unis |
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| Date conférence : |
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04/2012 |
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| Mots-clés : |
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MapReduce – cloud computing – data-intensive computing – hybrid infrastructures – BlobSeer – BitDew – Nimbus – HLCM – Grid'5000 |
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| Projet ANR : |
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| Référence du projet |
ANR-10-SEGI-001 |
| Année |
2010 |
| Acronyme du projet |
MapReduce |
| Titre du projet |
Traitement intensif de données à très grande échelle à l'aide du paradigme MapReduce sur des infrastructures de type cloud et hybrides |
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