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The widely discussed scientific data deluge creates not only a need to computationally scale an application from a local desktop or cluster to a supercomputer, but also the need to cope with variable data loads over time. Cloud computing offers a scalable, economic, on-demand model well matched to the evolving eScience needs. Yet cloud computing creates gaps that must be crossed to move science applications to the cloud. In this paper, the authors propose a Generic Worker framework to deploy and invoke science applications in the Cloud with minimal user effort and predictable, cost-effective performance.
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