Parametric Content-Based Publish/Subscribe

Abstract

Content-based publish/subscribe (CPS) is an appealing abstraction for building scalable distributed systems, e.g., message boards, intrusion detectors, or algorithmic stock trading platforms. Recently, CPS extensions have been proposed for location-based services like vehicular networks, mobile social networking, and so on. Although current CPS middleware systems are dynamic in the way they support the joining and leaving of publishers and subscribers, they fall short in supporting subscription adaptations. These are becoming increasingly important across many CPS applications. In algorithmic high frequency trading, for instance, stock price thresholds that are of interest to a trader change rapidly, and gains directly hinge on the reaction time to relevant fluctuations rather than fixed values. In location-aware applications, a subscription is a function of the subscriber location (e.g. GPS coordinates), which inherently changes during motion. The common solution for adapting a subscription consists of a resubscription, where a new subscription is issued and the superseded one canceled. This incurs substantial overhead in CPS middleware systems, and leads to missed or duplicated events during the transition. In this article, we explore the concept of parametric subscriptions for capturing subscription adaptations. We discuss desirable and feasible guarantees for corresponding support, and propose novel algorithms for updating routing mechanisms effectively and efficiently in classic decentralized CPS broker overlay networks. Compared to resubscriptions, our algorithms significantly improve the reaction time to subscription updates without hampering throughput or latency under high update rates. We also propose and evaluate approximation techniques to detect and mitigate pathological cases of high frequency subscription oscillations, which could significantly decrease the throughput of CPS systems thereby affecting other subscribers. We analyze the benefits of our support through implementations of our algorithms in two CPS systems, and by evaluating our algorithms on two different application scenarios.