Simulating live cloud adaptations prior to a production deployment using a models at runtime approach

The utilization of distributed resources, especially in the cloud, has become a best practice in research and industry. However, orchestrating and adapting running cloud infrastructures and applications is still a tedious and error prone task. Especially live adaptation changes need to be well tested before they can be applied on production systems. Meanwhile, a multitude of approaches exist that support the development of cloud applications, granting developers a lot of insight on possible issues. Nonetheless, not all issues can be discovered without performing an actual deployment. In this paper, we propose a model-driven concept that allows developers to assemble, test, and simulate the deployment and adaptation of cloud compositions without affecting the production system. In our concept, we reflect the production system in a runtime model and simulate all adaptive changes on a locally deployed duplicate of the model. We show the feasibility of the approach by performing a case study which simulates a reconfiguration of a computation cluster deployment. Using the presented approach, developers can easily assess how the planned adaptive steps and the execution of configuration management scripts affect the running system resulting in an early detection of deployment issues.