On the Design of an Architecture for Partitioned Knowledge Management in Autonomic Multimedia Access and Aggregation Networks

The recent emergence of multimedia services, such as Network Based Personal Video Recording and Broadcast TV over traditional DSL based access networks, has introduced stringent Quality of Experience (QoE) requirements. It is generally assumed that the wide variety of services and user profiles introduces the need for a per-user or per-subscriber QoE management. Such a complex QoE management requires real-time knowledge about the managed services, which is available amongst the different nodes in the network. However, even for managing a few services, a relatively large amount of, constantly updated, knowledge is needed. Propagating all the knowledge to all nodes is therefore not feasible. As not all knowledge is relevant to all nodes, it is important to perform an intelligent knowledge distribution and management. In this position paper, we introduce the concept of a cognitive model that describes the knowledge requirements of each node. Based on the information stated in this cognitive model, we discuss how filter queries, that typically describe what needs to be queried from other nodes, can be automatically generated leading to an efficient partitioning of the knowledge through the distributed nodes.