On Efficient Content Matching in Distributed Pub/Sub Systems

The efficiency of matching structures is the key issue for content publish/subscribe systems. In this paper, we propose an efficient matching tree structure, named CobasTree, for a distributed environment. Particularly, we model a predicate in each subscription filter as an interval and published content value as a data point. The CobasTree is designed to index all subscription intervals and a matching algorithm is proposed to match the data points to these indexed intervals. Through a set of techniques including selective multicast by bounding intervals, cost model-based interval division, and CobasTree merging, CobasTree can match the published contents against subscription filters with a high efficiency. We call the whole framework including CobasTree and the associated techniques as COBAS. The performance evaluation in simulation environment and PlanetLab environment shows COBAS significantly outperforms two counterparts with low cost and fast forwarding.