BridgeNet: An Adaptive Multi-Source Stream Dissemination Overlay Network

Emerging stream processing applications such as on-line data analysis often need to collect streaming information from geographically dispersed locations (e.g., different sensor networks). Different from conventional discrete data (e.g., messages), streaming data are time-varying and long-lived, which provides both new challenges and opportunities for optimizing wide-area continuous data dissemination. In this paper, we present BridgeNet, a novel biology-inspired stream dissemination overlay network that can dynamically learn stream patterns to achieve efficient multi-source stream dissemination. We propose a new distributed cell tree structure that can adaptively expand or contract itself in response to workload changes. BridgeNet performs pattern-based adaptations to deliver efficient stream disseminations without losing system stability. We have implemented a prototype of BridgeNet and conducted extensive experiments using both simulations and Planetlab deployment. The experimental results based on both synthetic workload and real data streams show that BridgeNet outperforms existing schemes for efficient multi-source stream dissemination.