LAUSR

Cloud Computing has assumed a relevant role in the ICT, profoundly influencing the life-cycle of modern applications in the manner they are designed, developed, and deployed and operated. In this article, we tackle the problem of supporting the design-time analysis of Cloud applications to identify a cost-optimized strategy for allocating components onto Cloud Virtual Machine infrastructural services, taking performance requirements into account. We present an approach and a tool, SPACE4Cloud, that supports users in modeling the architecture of an application, in defining performance requirements as well as deployment constraints, and then in mapping each architecture component into a corresponding VM service, minimizing total costs. An optimization algorithm supports the mapping and determines the Cloud configuration that minimizes the execution costs of the application over a daily time horizon. The benefits of this approach are demonstrated in the context of an industrial case study. Furthermore, we show that SPACE4Cloud leads to a cost reduction up to 60 percent, when compared to a first-principle technique based on utilization thresholds, like the ones typically used in practice, and that our solution is able to solve large problem instances within a time frame compatible with a fast-paced design process (less than half an hour in the worst case). Finally, we show that SPACE4Cloud is suitable to model even microservice-based applications and to compute the corresponding optimized deployment configuration which is compared with a state-of-the art meta-heuristic alternative method, achieving savings between 21 and 85 percent.