Reliability and Timeliness Analysis of Content-based Publish/subscribe Systems

Content-based Publish/subscribe systems (CBPS) is a simple yet powerful communication paradigm. Its content-centric nature is suitable for a wide spectrum of today’s content-centric applications such as stock market quote exchange, remote monitoring and surveillance, RSS news feed, and online gaming. As the trend shows that the amount of information along with its producers become astonishingly increasing everyday, a publish/subscribe system seems to be one of only a few viable choices that could govern the next-generation world of communication. However, the content-centric nature of a publish/subscribe system also poses difficulty in analyzing or assessing its performance. Moreover, the complexity increases when deploying a publish/subscribe system on top of best-effort, unreliable wide-area networks. Such uncertainty and complexity become a hindrance to apply content-based publish/subscribe systems to delay-sensitive applications that require reliable/timely event delivery and tight resource control such as soft real-time systems or cyber-physical systems. The need to solve such problem calls for a good analytical model that could capture both expressiveness and uncertainty nature of distributed CBPS systems yet predict the system behavior accurately. This dissertation is, to the best our knowledge, the first attempt to analyze the reliability/timeliness performance of distributed content-based publish/subscribe systems under best-effort networks. It proposes a probabilistic, analytical framework of content-based publish/subscribe systems under different dynamism for the purpose of performance analysis. Specifically, given a publish/subscribe system configuration and dynamism parameters, it estimates event delivery probability and timeliness received by each subscriber in the publish/subscribe system. The dissertation also presents evaluation results of the proposed predictive model via simulations with both synthetic traces and real-world traces. The results yield prediction accuracy and effectiveness of the proposed framework. The proposed analytical framework can be used as a tool for performance assessment or as a building block for publish/subscribe system optimizations such as subscriber admission control,