Scalable data management for map-reduce-based data-intensive applications: a view for cloud and hybrid infrastructures Online publication date: Wed, 24-Jul-2013
by Gabriel Antoniu; Alexandru Costan; Julien Bigot; Frédéric Desprez; Gilles Fedak; Sylvain Gault; Christian Pérez; Anthony Simonet; Bing Tang; Christophe Blanchet; Raphael Terreux; Luc Bougé; François Briant; Franck Cappello; Kate Keahey; Bogdan Nicolae; Frédéric Suter
International Journal of Cloud Computing (IJCC), Vol. 2, No. 2/3, 2013
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-optimised, 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.
Online publication date: Wed, 24-Jul-2013
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