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To Cloudify or Not to Cloudify: The Question for a Scientific Data Center

The idea of turning data centers executing scientific batch jobs into private clouds is as attractive as troubling. Cloud platforms may help both in limiting power consumption and in implementing fault tolerance strategies. However, there is also the fear that performance may worsen, and that the electricity required for longer job duration and fault tolerance implementation may overcome the saved one. In this paper, we present the consumability analysis for assessing the impact of cloud and fault tolerance tunings on scientific processing systems. The analysis considers performance, consumption, and dependability aspects, jointly. The aim is to pinpoint if, for a given system, there is a setting where consumption and job failure rate decrease, while performance is not affected. Applied to the scientific data center at our University, the analysis allowed us to find the proper selection of virtual machines' configuration, consolidation strategy, and fault tolerance tuning.