Semi-Probabilistic Content-Based Publish-Subscribe

Mainstream approaches to content-based distributed publish-subscribe typically route events deterministically based on information collected from subscribers, and do so by relying on a tree-shaped overlay network. While this solution achieves scalability in fixed, large-scale settings, it is less appealing in scenarios characterized by high dynamicity, e.g., mobile ad hoc networks or peer-to-peer systems. At the other extreme, researchers in the related fields of multicast and group communication have successfully exploited probabilistic techniques that provide increased fault tolerance, resilience to changes, and yet are scalable. In this paper, we propose a novel approach where event routing relies on deterministic decisions driven by a limited view on the subscription information and, when this is not sufficient, resorts to probabilistic decisions performed by selecting links at random. Simulations show that the particular mix of deterministic and probabilistic decisions we put forth in this work is very effective at providing high event delivery and low overhead in highly dynamic scenarios, without sacrificing scalability