Improving Scalability in Pub-Sub Knowledge-Based Networking by Semantic Clustering

The three main pub/sub systems – type-based, content-based and topic-based networks allow for normalised subscriptions and publications using a combination of push/pull message delivery. Knowledge-based Networks (KBN) extends content-based networks by allowing subscriptions to be matched not only on the contents of messages, but also on some semantics of the message contents [1,2,3]. This creates a fuller, richer and more meaningful system whereby publishers and subscribers can be matched using more a expressive subscription mechanism. Here we discuss how a Knowledge-Based Network implementation was extended to support node clustering based on subscription semantics, thereby improving performance and scalability. In extending the Knowledge-Based Network to incorporate semantic-based clustering, this research aims to provide a network environment in which routing nodes, publishers and subscribers are clustered based on their semantic footprint and interests. The benefits of this are twofold: Firstly, this reduces the processing time involved in making routing decisions based on messages content. Its takes fewer hops to get from source to destination, as these are already closely linked based on the likelihood of there being a match between the two. Secondly, this allows for natural grouping of likeminded publishers and subscribers as seen in traditional web forums / newsgroups. The cluster-based approach to pub/sub networks turns the normal user-based search paradigm full circle as network data is passed from node to node towards those who are most likely to be interested in the data as opposed to those users searching out that same data. An implementation of a KBN, based on the Siena CBN, has been implemented, and enables the efficient routing of distributed heterogeneous knowledge to, and only to, nodes that have expressed a specific interest in that knowledge. This KBN implementation currently operates on distributed PlanetLab Nodes. Initially clusters are statically designed and operated. In this sense nodes are assigned to clusters without the possibility of changing clusters once they have joined; later users will be able to join and leave clusters independently. Clusters will then be seen as organic structures in which users join and leave as their own personal interests change, grow, reform and are refined. Preliminary evaluations show the importance of semantic clustering for efficient performance and network scalability. These evaluations demonstrate how even inflexible and static clustering can have a substantial positive effect. Ongoing research is focusing on how clustering can be performed dynamically as the semantics of the data in the network changes. …