No abstract

Distributed publish/subscribe systems are usually deployed on top of an overlay network that enables complex routing strategies implemented in the application layer. Up to now, only little effort has been spent on the design of the broker overlay network assuming that it is either static or manually administered. As publish/subscribe systems are increasingly targeted at dynamic environments where client behavior and network characteristics vary over time, static overlay networks lead to suboptimal performance. In this paper, we present a self-organizing broker overlay infrastructure that adapts dynamically to achieve a better efficiency on both, the application and the network layer. This is obtained by taking network metrics as well as notification traffic into account.

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Publish/subscribe communication is increasingly often the basis for distributed, event-based, and loosely-coupled applications. With this communication paradigm, application components communicate indirectly by publishing notifications and by subscribing to those notifications of interest. To precisely specify the subscribers' interests, content-based publish/subscribe systems provide expressive filter models that, on the one hand, ease implementing versatile and entangled interaction patterns. On the other hand, however, resulting notification flows evolve dynamically and implicitly making an analysis the more challenging and complex.In this paper, we focus on modeling the interrelationship between the notifications being advertised, produced, and requested by publishers and subscribers, respectively. The presented analytical models, in their general form, support various data and filter models to compute matching and forwarding probabilities, which provide the basis to derive message rates as well as more advanced performance metrics. To demonstrate their applicability, we instantiate the general models with a specific data and filter model based on a non-uniform, multi-dimensional distribution of published and subscribed notifications. Our results show the impact of the distributions' skewness and, thereby, prove the necessity to precisely model the content interrelationships in order to make reliable performance predictions.

No abstract