Utilising Stream Reasoning Techniques to Create a Self-Adaptation Framework for Cloud Environments

As cloud computing systems are reaching the stage where the human effort required to maintain them at an operational level is unsupportable, one of the major challenges faced by cloud computing providers is to develop appropriate mechanisms for run-time monitoring and adaptation, to prevent cloud platforms from quickly dissolving into a non-reliable environment. In this context, the application of intelligent approaches to Autonomic Clouds may offer promising opportunities. In this paper we present a novel approach to provide cloud platforms with autonomic capabilities based on utilising techniques from the domains of the Semantic Web and Stream Reasoning. The main idea of this approach is to encode values, monitored within cloud platforms, with Semantic Web languages, which will allow us to integrate these semantically enriched observation streams with static ontological knowledge and apply intelligent reasoning. Based on such run-time reasoning capabilities, we are able to perform analysis and failure diagnosis, and suggest further adaptation actions. As aninitial proof of concept, we sketch out a conceptual architecture for a self-adaptation framework and introduce a prototype solution implementing this architecture.