A reinforcement learning based self-healing algorithm for managing context adaptation
Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
An energy aware context model for green IT service centers
ICSOC'10 Proceedings of the 2010 international conference on Service-oriented computing
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This paper proposes a generic policy based self-management model that can be used to automatically detect and repair the problems appeared during the context adaptation processes. To successfully capture and evaluate the dynamic rules that govern the context aware adaptation processes we have defined an generic context policy representation model and its associated reasoning language conversion model for run-time evaluation. To evaluate the run-time degree of respecting the context policies we define and formalize the concept of context entropy. The context information is modeled in a system programmatic manner using both the set based and ontology based representations provided by our RAP (Resources, Actors, Policies) context model. The context model artifacts are generated and administrated at run time by a management infrastructure based on BDI (Believe, Desire, Intentions) agents. The model was tested and validated within the premises of our Distributed Systems Research Laboratory smart environment.