SemCast: semantic multicast for content-based data dissemination

Abstract

We address the problem of content-based dissemination of highly-distributed, high-volume data streams for stream-based monitoring applications and large-scale data delivery. Existing content-based dissemination approaches commonly rely on distributed filtering trees that require filtering at all brokers on the tree. We present a new semantic multicast approach that eliminates the need for content-based filtering at interior brokers and facilitates fine-grained control over the construction of efficient dissemination trees. The central idea is to split the incoming data streams (based on their contents, rates, and destinations) and then spread the pieces across multiple channels, each of which is implemented as an independent dissemination tree. We present the basic design and evaluation of SemCast, an overlay-network based system that implements this semantic multicast approach. Through a detailed simulation study and realistic network topologies, we demonstrate that SemCast significantly improves the efficiency of dissemination compared to traditional approaches.