RRPS: A Ranked Real-Time Publish/Subscribe Using Adaptive QoS

Abstract

Publish-Subscribe paradigm has been widely employed in Real-Time applications. However, the existing technologies and models only support a simple binary concept of matching: an event either matches a subscription or it does not; for instance, a production monitoring event will either match or not match a subscription for production anomaly. Based on adaptive Quality of Service (QoS) management, we propose a novel publish/subscribe model, which is implemented as a critical service in a real-time database Agilor. We argue that publications have different relevance to a subscription. On the premise of guaranteeing deadline d, a subscriber approximately receives k most relevant publications, where k and d are parameters defined by each subscription. After the architecture of our model is described, we present negotiations between components and scalable strategies for adaptive QoS management. Then, we propose an efficient algorithm to select different strategies adaptively depending on estimation of current QoS. Furthermore, we experimentally evaluate our model on real production data collected from manufacture industry to demonstrate its applicability in practice.KeywordsData Distribution ServiceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.