Integrating semantic run-time models for adaptive software systems

Software-intensive systems work in ever-changing environments requiring expensive technical efforts to manage their evolution. In order to mitigate their risks and costs they should dynamically self-adapt to any modification of their environment. MAPE-K (Monitor, Analyze, Plan, Execute - Knowledge) is the basic architectural pattern for building software-intensive self-adaptable systems. In this paper we propose an approach in which all the information about a system and its environment is unified by using Semantic Web technologies into a set of semantic run-time models which enhance the Knowledge in MAPE-K. Ontologies are used to manage the interaction and integration of these models with disparate data sources. The resulting knowledge base is then used to drive adaptation activities exploiting well known languages and notations. We discuss how MAPE-K can be exploited in order to take advantage of ontological representations, along with Semantic Web languages and tools, by studying a real-word case study: a legacy system that was not designed to perform automatic adaptation. We discuss merits and limits of our approach based on semantic run-time models both in the context of this specific case study and in a broader scope.