Abstract. The model-driven engineering community has developed expressive model transformation techniques based on metamodels, which ease the specification of translations between different model types. Thus, it is attractive to also apply these techniques for autonomic and selfadaptive systems at run-time to enable a comprehensive monitoring of their architectures while reducing development efforts. This requires special solutions for model transformation techniques as they are applied at run-time instead of their traditional usage at development time. In this paper we present an approach to ease the development of architectural monitoring based on incremental model synchronization with triple graph grammars. We show that the provided incremental synchronization between a running system and models for different self-management capabilities provides a significantly better compromise between performance and development costs than manually developed solutions.

Abstract. Software evolution and the resulting need to continuously adapt the software is one of the main challenges for software engineering. The model-driven development movement therefore aims at improving the longevity of software by keeping the development artifacts more consistent and better changeable by employing models and to a certain degree automated model operations. Another trend are systems that tackle the challenge at runtime by being able to adapt their structure and behavior to be more flexible and operate in more dynamic environments (e.g., context-aware software, autonomic computing, self-adaptive software). Finally, models at runtime, where the benefits of model-driven development are employed at runtime to support adaptation capabilities, today lead towards a unification of both ideas. In this paper, we present graph transformations and show that they can be employed to engineer solutions for all three outlined cases. Furthermore, we will even be able to demonstrate that graph transformation based technology has the potential to also unify all three cases in a single scenario where models at runtime and runtime adaptation is linked with classical MDE. Therefore, we at first provide an introduction in graph transformations, then present the related techniques of Story Pattern and Triple Graph Grammars, and demonstrate how with the help of both techniques model transformations, adaptation behavior and runtime model framework work. In addition, we show that due to the formal underpinning analysis becomes possible and report about a number of successful examples.

Architectural monitoring and adaptation allows self-management capabilities of autonomic systems to realize more powerful adaptation steps, which observe and adjust not only parameters but also the software architecture. However, monitoring as well as adaptation of the architecture of a running system in addition to the parameters are considerably more complex and only rather limited and costly solutions are available today. In this paper we propose a model-driven approach to ease the development of architectural monitoring and adaptation for autonomic systems. Using meta models and model transformation techniques, we were able to realize an incremental synchronization between the run-time system and models for different self-management activities. The synchronization might be triggered when needed and therefore the activities can operate concurrently.