Abstract
Subscription adaptations are becoming increasingly important across many content-based publish/subscribe (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. The common solution to adapt a subscription consists of a re-subscription, where a new subscription is issued and the superseded one canceled. This is ineffective, leading to missed or duplicate events during the transition. In this paper, we introduce the concept of parametric subscriptions to support subscription adaptations. We propose novel algorithms for updating routing mechanisms effectively and efficiently in classic CPS broker overlay networks. Compared to re-subscriptions, our algorithms significantly improve the reaction time to subscription updates and can sustain higher throughput in the presence of high update rates. We convey our claims through implementations of our algorithms in two CPS systems, and by evaluating them on two different real-world applications.
This research is supported, in part, by the National Science Foundation (NSF) under grant #0644013 and #0834529. Any opinions, findings, conclusions, or recommendations in this paper are those of the authors and do not necessarily reflect the views of the NSF.
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Jayaram, K.R., Jayalath, C., Eugster, P. (2010). Parametric Subscriptions for Content-Based Publish/Subscribe Networks. In: Gupta, I., Mascolo, C. (eds) Middleware 2010. Middleware 2010. Lecture Notes in Computer Science, vol 6452. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16955-7_7
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