On collaborative content distribution using multi-message gossip
We study epidemic schemes in the context of collaborative data delivery. In this context, multiple chunks of data reside at different nodes, and the challenge is to simultaneously deliver all chunks to all nodes. Here we explore the inter-operation between the gossip of multiple, simultaneous message-chunks. In this setting, interacting nodes must select which chunk, among many, to exchange in every communication round. We provide an efficient solution that possesses the inherent robustness and scalability of gossip. Our approach maintains the simplicity of gossip, and has low message, connections and computation overhead. Because our approach differs from solutions proposed by network coding, we are able to provide insight into the tradeoffs and analysis of the problem of collaborative content distribution. We formally analyze the performance of the algorithm, demonstrating its efficiency with high probability.
collaborative content distribution gossip message dissemination epidemic algorithms randomized algorithms