Towards Pattern-Based Optimization of Cloud Applications

With the promise of seemingly unlimited resources and the flexible pay-as-you-go business model, more and more applications are moving to the cloud. However, to fully utilize the features offered by cloud providers, the existing applications need to be adapted accordingly. To support the developer in this task, different cloud computing patterns have been proposed. Nevertheless, selecting the most appropriate patterns and their configuration is still a major challenge. This is further complicated by the costs usually associated with deploying and testing an application in the cloud. In this paper, we encode the pattern selection problem as a model-based optimization problem to automatically compute good solutions of configured pattern applications. Particularly, we propose a two-phased approach, which is guided by user-defined constraints on the non-functional properties of the application. In the first phase, a preliminary set of promising solutions is computed using a genetic algorithm. In the second phase, this set of solutions is evaluated in more detail using model simulation. We demonstrate the proposed approach and show its feasibility by an initial case study.