In content-based publish-subscribe (pub/sub) systems, users express their interests as queries over a stream of publications. Scaling up content-based pub/sub to very large numbers of subscriptions is challenging: users are interested in low latency, that is, getting subscription results fast, while the pub/sub system provider is mostly interested in scaling, i.e., being able to serve large numbers of subscribers, with low computational resources utilization. We present a novel approach for scalable content-based pub/sub in the presence of constraints on the available CPU and network resources, implemented within our pub/sub system Delta. We achieve scalability by off-loading some subscriptions from the pub/sub server, and leveraging view-based query rewriting to feed these subscriptions from the data accumulated in others1. Our main contribution is a novel algorithm for organizing views in a multi-level dissemination network, exploiting view-based rewriting and powerful linear programming capabilities to scale to many views, respect capacity constraints, and minimize latency. The efficiency and effectiveness of our algorithm are confirmed through extensive experiments and a large deployment in a WAN.@en

We consider the problem of efficiently sharing large volumes of XML data based on distributed hash table overlay networks. Over the last three years, we have built ViP2P (standing for Views in Peer-to-Peer), a platform for the distributed, parallel dissemination of XML data among peers. At the core of ViP2P stand distributed materialized XML views, defined as XML queries, filled in with data published anywhere in the network, and exploited to efficiently answer queries issued by any network peer. ViP2P is one of the very few fully implemented P2P platforms for XML sharing, deployed on hundreds of peers in a WAN. This paper describes the system architecture and modules, and the engineering lessons learned. We show experimental results, showing that our choices, outperf related systems by orders of magnitude in terms of data volumes, network size and data dissemination throughput.@en