An approach for semantic interoperability in autonomic distributed intelligent systems

The complexity and dynamism of distributed intelligent systems have motivated the utilization of autonomic computing in such systems. However, the interoperability of agents in order to attain self-managing properties is itself a great challenge that requires more attention. Although a number of semantic approaches exist for central autonomic systems, there are not many attempts to develop mechanisms for facilitating semantic interoperability to attain a distributed feedback loop. In this paper, a semantic approach is presented to overcome this challenge. The main component of this approach is AutonoML language that provides the shared semantics for autonomic computing in distributed environments. Besides this language, the method of its application for enhancing the interoperability of the distributed MAPE-K loop is presented. In order to evaluate this mechanism, three well-known metrics from the ontological assessment area are utilized, and the results are compared with an existing autonomic ontology that shows greater relational and attributes richness of the proposed semantic structures. Moreover, in order to evaluate the applicability of the mechanism, a case from NASA-ANTS project is studied, and the results show that the proposed mechanism is capable of facilitating the achievement of more stable self-adaptive solutions in less time and by exchanging fewer messages.