Semantic Run-Time Models for Self-Adaptive Systems: A Case Study

Abstract

Today's software systems increasingly work in changing environments, where rapid modifications in user needs, resource variabilities and system faults require remarkable administrative efforts. In order to mitigate the costs for governing these activities, software systems are expected to dynamically self-adapt. The problem of supporting auto-adaptation, which is complex activity in itself, is further exacerbated when applied to legacy systems which have not been developed for this purpose. In this paper we introduce a novel approach to self-adaptation based on the MAPE-K paradigm, where semantic models are used to provide an unified view of the heterogeneous elements composing these systems, and reasoning mechanisms are leveraged to drive adaptation strategies. We present the implementation of an adaptation engine based these concepts that uses ontologies and Semantic Web technologies, and discuss its application in a real world case study. From this experience, we offer recommendations for future research in this area.