Towards Scalable Data Management for Map-Reduce-based Data-Intensive Applications on Cloud and Hybrid Infrastructures - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2012

Towards Scalable Data Management for Map-Reduce-based Data-Intensive Applications on Cloud and Hybrid Infrastructures

Gabriel Antoniu
Luc Bougé
Kate Keahey
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  • PersonId : 884576
Frédéric Suter

Abstract

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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Dates and versions

hal-00684866 , version 1 (20-04-2012)

Identifiers

  • HAL Id : hal-00684866 , version 1

Cite

Gabriel Antoniu, Julien Bigot, Christophe Blanchet, Luc Bougé, François Briant, et al.. Towards Scalable Data Management for Map-Reduce-based Data-Intensive Applications on Cloud and Hybrid Infrastructures. 1st International IBM Cloud Academy Conference - ICA CON 2012, Apr 2012, Research Triangle Park, North Carolina, United States. ⟨hal-00684866⟩
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