C Ov Er F E At U Re Models At Runtime

Abstract

Our research shows that autonomic behavior can be achieved by leveraging variability models at runtime. In this way, the modeling effort made at design time is not only useful for producing the system but also provides a richer semantic base for autonomic behavior during execution. The use of variability models at runtime brings new opportunities for autonomic capabilities by reutilizing the efforts invested at design time. Our proposed approach has two aspects: reuse of design knowledge to achieve AC and reuse of existing model-management technologies at runtime. We developed the Model-Based Reconfiguration Engine (MoRE) to implement model-management operations. Our research demonstrates the approach's feasibility for smart homes, especially for self-healing and -configuring capabilities.