A general scalable and elastic matching service for content-based publish/subscribe systems

Characterized by the emergence of a large number of live content, the emergency applications have received increasing attention in recent years. Providing a general and scalable event, matching service can precisely notify users latest information that they are interested in. However, because the live content arrival rate may churn significantly in a short time and subscriptions with various patterns tend to be skewed, it is challenging to increase the generality, scalability, and elasticity of the matching process. We propose a novel parallel event matching service based on the cloud computing environment, called GSEM, to satisfy these requirements. GSEM first presents a two-hop framework and a general subscription pattern to handle various patterns of subscriptions. To provide scalable matching service, a hybrid content space partition scheme is proposed to divide large skewed subscriptions into multiple small clusters managed by a group of parallel servers. To adapt to the sudden change of event arrival rate, GSEM elastically adjusts the scale of servers and rebalances their workloads through a performance-aware detection technique. A prototype deployment on the OpenStack platform shows that GSEM achieves scalable matching throughput with the growth of servers, elastic service capacity with the change of event arrival rate, and significantly outperforms the existing cloud based systems in various workloads. Copyright (C) 2014 John Wiley & Sons, Ltd.