Systems Modeling Language (SysML) is an object management group standard for systems-of-systems engineering. It enables the description of complex system models; however, it cannot effectively support all system engineering activities. For instance, system performance evaluation is usually performed via simulation. In this case, the transformation of SysML system models to executable simulation models for specific simulation methodologies and tools is required. Model transformation is a key component for addressing the challenges of seamless integration of SysML model simulation in model-based system engineering. In this paper, we explore a declarative approach, based on the query/view/transformation-relations (QVT-R) standard, for the transformation of SysML models to executable simulation models, fully adhering model-driven architecture (MDA) concepts. It is supported by a framework implemented to provide executable simulation model and code generation. Methodological guidelines, for the effective use of a declarative language as QVT-R, for model transformation, are provided, emphasizing the utilization of existing domain-specific SysML profiles, as well as executable simulation library components. The experience obtained from two different domains, namely, enterprise information and railway transportation systems, modeled as systems-of-systems via SysML, is discussed, based on a quantitative analysis of the respective QVT-R transformations.

As cloud computing has grown in popularity, several different models and deployment strategies have emerged, fulfilling specific needs of different users. Thus cloud service selection is a major task that combines business and technical aspects. Several cloud providers categorize their bundles to help users find the best fit. However, the policy behind this categorization is unclear, making it difficult to be consistent among providers. Therefore, this work explores the complication of selecting the optimal cloud service among numerous and comparable solutions and introduces a selection framework for cloud services based on clustering analysis, providing an overall size categorization of cloud services derived from the cloud market and making the categorization policy explicit and homogeneous across all providers. Finally, a real-word case study is presented, highlighting the application of the proposed framework.