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Parametric Subscriptions for Content-Based Publish/Subscribe Networks

  • K. R. Jayaram
  • Chamikara Jayalath
  • Patrick Eugster
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6452)

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 The common solution to adapt a subscription consists of a re-subscription, where a new subscription is issued and the superseded one canceled.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.

Keywords

Navigation System Binary Decision Diagram Spurious Event Algorithmic Trading High Frequency Trading 
These 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.

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Copyright information

© IFIP International Federation for Information Processing 2010

Authors and Affiliations

  • K. R. Jayaram
    • 1
  • Chamikara Jayalath
    • 1
  • Patrick Eugster
    • 1
  1. 1.Department of Computer SciencePurdue UniversityUSA

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