Provenance for MapReduce-based data-intensive workflows
Proceedings of the 6th workshop on Workflows in support of large-scale science
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Domain scientists synthesize different data and computing resources to solve their scientific problems. Making use of distributed execution within scientific workflows is a growing and promising way to achieve better execution performance and efficiency. This paper presents a high-level distributed execution framework, which is designed based on the distributed execution requirements identified within the Kepler community. It also discusses mechanisms to make the presented distributed execution framework easy-to-use, comprehensive, adaptable, extensible and efficient.