Ontological Semantics for Distributing Contextual Knowledge in Highly Distributed Autonomic Systems

Much recent research has focused on applying Autonomic Computing principles to achieve constrained self-management in adaptive systems, through self-monitoring and analysis, strategy planning, and self adjustment. However, in a highly distributed system, just monitoring current operation and context is a complex and largely unsolved problem domain. This difficulty is particularly evident in the areas of network management, pervasive computing, and autonomic communications. This paper presents a model for the filtered dissemination of semantically enriched knowledge over a large loosely coupled network of distributed heterogeneous autonomic agents, removing the need to bind explicitly to all of the potential sources of that knowledge. This paper presents an implementation of such a knowledge delivery service, which enables the efficient routing of distributed heterogeneous knowledge to, and only to, nodes that have expressed an interest in that knowledge. This gathered knowledge can then be used as the operational or context information needed to analyze to the system’s behavior as part of an autonomic control loop. As a case study this paper focuses on contextual knowledge distribution for autonomic network management. A comparative evaluation of the performance of the knowledge delivery service is also provided.