Parallel Ensemble Matching Based on Subscription Partitioning for Content-Based Publish/Subscribe Systems

Abstract

The content-based publish/subscribe system is 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 contained in subscriptions are handled the same way in most existing data structures, without considering their differences in matching probability. In this paper, we propose the concept of parallel ensemble matching (PEM) based on subscription partitioning. The basic idea is that we have the right algorithm handling the right subscriptions at the right time. First of all, we design a PEM framework by classifying subscriptions according to their matching probabilities and use the proper algorithms to process each subscription category. Furthermore, to deal with high-dimensional subscriptions, we propose a fine-grained PEM (fgPEM) that exploits matching algorithms with complementary behaviors by partitioning subscriptions into sub-subscriptions. We implement the prototype of PEM and fgPEM based on two existing algorithms. The experiment results show that PEM improves the matching performance by 43%. On the basis of PEM, fgPEM further improves the performance by 31%.