Model-Driven Architectural Monitoring and Adaptation for Autonomic Systems
This work addresses the problem of limited and costly solutions for architectural adaptation in autonomic systems, representing an incremental improvement in model-driven engineering for self-management.
The paper tackles the complexity of architectural monitoring and adaptation in autonomic systems by proposing a model-driven approach using meta models and model transformation techniques, enabling incremental synchronization between runtime systems and models for concurrent self-management activities.
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.