Cloud computing plays an increasingly important role in realizing scientific applications by offering virtualized\ncompute and storage infrastructures that can scale on demand. This paper presents a self-configuring adaptive\nframework optimizing resource utilization for scientific applications on top of Cloud technologies. The proposed\napproach relies on the concept of utility, i.e., measuring the usefulness, and leverages the well-established principle\nfrom autonomic computing, namely the MAPE-K loop, in order to adaptively configure scientific applications. Therein,\nthe process of maximizing the utility of specific configurations takes into account the Cloud stack: the application layer,\nthe execution environment layer, and the resource layer, which is supported by the defined Cloud stack configuration\nmodel. The proposed framework self-configures the layers by evaluating monitored resources, analyzing their state,\nand generating an execution plan on a per job basis. Evaluating configurations is based on historical data and a utility\nfunction that ranks them according to the costs incurred. The proposed adaptive framework has been integrated into\nthe Vienna Cloud Environment (VCE) and the evaluation by means of a data-intensive application is presented herein.
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