Adaptive Knowledge Bases in Self-Adaptive System Design

Self-adaptive systems allow for flexible solutions in changing environments. Usually, a fixed set of predefined rules is used to define the adaptation possibilities of a system. The main problem of such systems is to cope with environment behaviours that were not anticipated at design-time. In this case, no adaptation rule might be applicable or adaptations might not have the expected effect. In this paper, we propose an extended architecture of IBM's MAPE-K loop to cope with this problem. We impose a structure on the knowledge base consisting of an abstract system and environment model, a global goal model, and a set of (current) adaptation rules. Furthermore, we introduce an evaluation component that deletes failed adaptation rules, as well as a learning component that uses run-time models to autonomously generate new rules if the current ones are not applicable. With our approach, not only functional components can dynamically be adapted but also the adaptation logic itself.