Ontological generation of filter rules for context exchange in autonomic multimedia networks

Network management has suffered from increases in business, system, and operational complexity. This has been exacerbated by the heterogeneity in management data as well as the high quality requirements of multimedia services. Autonomic networking manages this growing complexity by adding intelligence inside network nodes and network management applications. While most autonomic applications simply use a control loop to monitor and configure entities, our work is aimed at building a self-governing network that is able to fulfill the requirements of current and future services. This means that management applications need a detailed and dynamic view of the contextual status of the network nodes as a whole in order to adapt their behaviour to changing context. In this paper, we propose an algorithm to semi-automatically generate filter rules based on existing information in a network management information model. These filter rules are used to determine the set of contextual data that needs to be exchanged with other nodes. The algorithm exploits the reasoning capabilities of ontologies and relies on the introduction of additional semantic relationships to achieve a fine-grained context exchange model. Large scale evaluations were conducted to characterise the performance of this ontological approach.