Towards Adaptable Data Farming in Clouds

Parameter study is a widely spread type of scientific research methodologies used with modern High Performance and High Throughput Computing infrastructures such as Clouds. More and more often, parameter study experiments are oriented towards generating large amount of data describing complicated processes and phenomena. It becomes clear that new software for supporting such large-scale experiments is required. In this paper, we propose an enhancement of the methodology based on parameter studies of conducting scientific research called data farming in regard to its adaptability features, along with an accompanying software which supports different phases of the enhanced methodology.