Comat: An Effective Composite Matching Framework for Content-based Pub/Sub Systems

Content-based publish/subscribe systems provide subscribers with the ability to express their interests, but event matching operations need to be performed in a timely manner. To achieve a high matching speed, many efficient algorithms have been proposed. However, the performance of most algorithms is affected by the matching probability of subscriptions with events. Aiming to provide a fast and stable matching performance, in this paper, we propose an effective composite matching framework called Comat which explores the idea of performing event matching using multiple algorithms possessing complementary behavior to the matching probability of subscriptions. Comat predicts the matching time of the algorithms for each event and chooses the one that matches the event with the minimal cost. We implement a prototype of Comat based on two existing algorithms and TensorFlow. The experiment results show that Comat improves and stabilizes the matching performance by 25 % and 30 % respectively on average, well achieving our design goal.