  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.
