ABSTRACT Domain The deployment of distributed multi‐component cloud applications typically requires a combination of multiple heterogeneous deployment technologies. A different combination of deployment technologies should be chosen due to varying deployment qualities, such as the functional suitability and reliability of the deployment technologies for the deployment of the components. A suboptimal selection of deployment technologies makes the deployment error‐prone. Problem Selecting and maintaining the combination of deployment technologies requires modeling effort and expertise. Contributions We present a method that automatically selects deployment technologies based on a knowledge base of deployment scenarios and corresponding deployment qualities of deployment technologies. Evaluation We show the practical applicability and the usefulness of our method. For the practical applicability, we conduct two case studies based on an open‐source reference architecture application and a real‐world industry application using a prototypical implementation of our method. For the usefulness, we conduct a user study in which participants assign deployment technologies with and without our method. Conclusions Our method is a useful contribution that reduces the modeling effort and the required expertise when maintaining the combination of deployment technologies. Further, our method enhances the deployment quality.

Demonstrating the real-time execution of the software in distributed cyber-physical systems like vehicles in a realistic way, e.g., in the context of teaching students at universities, often requires a real car which is costly and hard to acquire. Therefore, we present an approach for using the MechatronicUML to model structural and behavioral aspects of the software for vehicles and generate code that we deploy and run on robot cars in our laboratory environment. We demonstrate this approach with an exemplary application scenario that comprises two vehicles driving on a road and exchanging information to prepare for a cooperative overtaking through a remotely deployed message broker. After adding support for our robot car hardware platform in an existing code generator for MechatronicUML, the robot cars could execute this scenario as envisioned with the generated code. The approach allows us to model distributed cyber-physical systems in a realistic way while demonstrating them in a simplified setup that is accessible to students. A video showcasing this demonstration can be accessed at https://youtu.be/8XgSudDxDB4.