A Model Driven Approach for Supporting the Cloud Target Selection Process

The decision making process for the selection of one cloud target over another plays a major role during the migration to the Cloud, affecting not only the operational costs, functional characteristics and QoS, but also the development, monitoring and maintaining experience of the IT professionals. As the Cloud gains ground, a progressively growing number of cloud providers, services and technologies are exposed in the market rendering the research and selection upon them complex and time consuming. Proposed efforts for automatic support, fail to follow the quick paste of evolution, demanding, thus, even more effort for maintaining the supporting systems. In this paper the Cloud Target Selection (CTS) tool methodology and prototype implementation are presented introducing a novel approach: The CloudML@artist modeling language is exploited as a representation of real-world cloud environments becoming a source of information for an extensible decision making mechanism. The proposed work contributes in the direction towards the construction of an adaptive solution, which will follow the technological advances requiring the minimum of human intervention.