Efficient Filtering in Publish-Subscribe Systems Using Binary Decision

Abstract

Implicit invocation or publish-subscribe has become an important architectural style for large-scale system design and evolution. The publish-subscribe style facilitates developing large-scale systems by composing separately developed components because the style permits loose coupling between various components. One of the major bottlenecks in using publish-subscribe systems for very large scale systems is the efficiency of filtering incoming messages, i.e., matching of published events with event subscriptions. This is a very challenging problem because in a realistic publish subscribe system the number of subscriptions can be large. We present an approach for matching published events with subscriptions which scales to a large number of subscriptions. Our approach uses binary decision diagrams, a compact data structure for representing Boolean functions which has been successfully used in verification techniques such as model checking. Experimental results clearly demonstrate the efficiency of our approach.