Cloud application deployment is becoming increasingly popular for the removal of hardware costs, the pay-per-use cost model and their ability to scale. However deploying on the Cloud carries both opportunities and threads regarding to energy efficiency. In order to help Cloud application developers to learn to reason about how much energy is consumed by their application on the server-side, we have developed a framework centered on a UML profile for relating energy goals, requirements and associated KPI metrics to application design and deployment elements. Our previous work has focused on the use of such a framework for carrying our run-time experiments to select the best approach.
In this paper, we explore the feasibility of a complementary approach able to provide support at design time based on a finer grained deployment models, the specification of Cloud and energy adaptation policies and the use of a discrete event simulator able to reason on key performance indicators such as energy but also overall performance, delay and costs. The goal is to support the Cloud developer in pre-selecting the best trade-off that can be further tuned at run-time.