PEM: A Parallel Ensemble Matching Framework for Content-based Publish/Subscribe Systems

Content-based publish/subscribe systems are an effective paradigm for implementing on-demand event distribution.Each event needs to be matched against subscriptions to identify the target subscribers.To improve the matching performance, many novel data structures have been proposed.However, the predicates included in subscriptions are handled the same way in most existing data structures, which is not efficient given the matching probability of predicates.In this paper, we propose a parallel ensemble matching framework called PEM, which uses multiple algorithms with complementary behavior on predicate matching probabilities.To achieve the performance balance of parallel matching, we design an elastic subscription classification method.We implement a prototype of PEM based on two existing algorithms.The experiment results show that PEM improves the matching performance by 43%.