Scalable data management for map-reduce-based data-intensive applications: a view for cloud and hybrid infrastructures. (1st January 2013) - Antoniu, Gabriel; Costan, Alexandru; Bigot, Julien; Desprez, Frédéric; Fedak, Gilles; Gault, Sylvain; Pérez, Christian; Simonet, Anthony; Tang, Bing; Blanchet, Christophe; Terreux, Raphael; Bougé, Luc; et al.
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.