Abstract
Scientific applications are increasingly using cloud resources for their data analysis workflows. However, managing data effectively and efficiently over these cloud resources is challenging due to the myriad storage choices with different performance-cost trade-offs, complex application choices, complexity associated with elasticity and, failure rates. The explosion in scientific data coupled with unique characteristics of cloud environments require a more flexible and robust distributed data management solution than the ones currently in existence. This paper describes the design and implementation of FRIEDA - a Flexible Robust Intelligent Elastic Data Management framework. FRIEDA coordinates data in a transient cloud environment taking into account specific application characteristics. Additionally, we describe a range of data management strategies and show the benefit of flexible data management schemes in cloud environments. We study two distinct scientific applications from bioinformatics and image analysis to understand the effectiveness of such a framework.
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