Methodology for trade-off analysis when moving scientific applications to Cloud

Scientific applications have always been one of the major driving forces for the development and efficient utilization of large scale distributed systems - computational Grids represent one of the prominent examples. While these infrastructures, such as Grids or Clusters, are widely used for running most of the scientific applications, they still use bare physical machines with fixed configurations and very little customizability. Today, Clouds represent another step forward in advanced utilization of distributed computing. They provide a fully customizable and self-managing infrastructure with scalable on-demand resources. However, true benefits and trade-offs of running scientific applications on a cloud infrastructure are still obscure, due to the lack of decision making support, which would provide a systematic approach for comparing these infrastructures. In this paper we introduce a comprehensive methodology for comparing the costs of using both infrastructures based on resource and energy usage, as well as their performance. We also introduce a novel approach for comparing the complexity of setting up and administrating such an infrastructure.