An adaptive peer-sampling protocol for building networks of browsers

Peer-sampling protocols constitute a fundamental mechanism for a number of large-scale distributed applications. The recent introduction of WebRTC facilitated the deployment of decentralized applications over a network of browsers. However, deploying existing peer-sampling protocols on top of WebRTC raises issues about their lack of adaptiveness to sudden bursts of popularity over a network that does not manage addressing or routing. Spray is a novel random peer-sampling protocol that dynamically, quickly, and efficiently self-adapts to the network size. Our experiments show the flexibility of Spray and highlight its efficiency improvements at the cost of small overhead. We embedded Spray in a real-time decentralized editor running in browsers and ran experiments involving up to 600 communicating Web browsers. The results demonstrate that Spray significantly reduces the network traffic according to the number of participants and saves bandwidth.