Rappel: Exploiting interest and network locality to improve fairness in publish-subscribe systems

Abstract

In this paper, we present the design, implementation and evaluation of Rappel, a peer-to-peer feed-based publish-subscribe service. By using a combination of probabilistic and gossip-like techniques and mechanisms, Rappel provides noiselessness, i.e., updates from any feed are received and relayed only by nodes that are subscribers of that feed. This leads to a fair system: the overhead at each subscriber node scales with the number and nature of its subscriptions. Moreover, Rappel incurs small publisher and client overhead, and its clients receive updates quickly and with low IP stretch. To achieve these goals, Rappel exploits ''interest locality'' characteristics observed amongst real multi-user multi-feed populations. This is combined with systems design decisions that enable nodes to find other subscribers, and maintain efficient network locality-aware dissemination trees. We evaluate Rappel via both trace-driven simulations and a PlanetLab deployment. The experimental results from the PlanetLab deployment show that Rappel subscribers receive updates within hundreds of milliseconds after posting. Further, results from the trace-driven simulator match our PlanetLab deployment, thus allowing us to extrapolate Rappel's performance at larger scales.