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

  • Authors:

  • Xinjie Lu;Xin Li;Tian Yang;Zaifei Liao;Wei Liu;Hongan Wang

  • Affiliations:

  • Institute of Software, Chinese Academy of Sciences, Beijing, China 100190 and Graduate University of the Chinese Academy of Sciences, Beijing, China 100049;Department of Computer Science and Technology, Shandong University, Jinan Shandong, China 250101;Institute of Software, Chinese Academy of Sciences, Beijing, China 100190 and Graduate University of the Chinese Academy of Sciences, Beijing, China 100049;Institute of Software, Chinese Academy of Sciences, Beijing, China 100190 and Graduate University of the Chinese Academy of Sciences, Beijing, China 100049;Institute of Software, Chinese Academy of Sciences, Beijing, China 100190;Institute of Software, Chinese Academy of Sciences, Beijing, China 100190 and State Key Lab. of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China 100190

  • Venue:
  • ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

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.