We study epidemic schemes in the context of collaborative data delivery. In this context, multiple chunks of data reside at di\ufb00erent 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 e\ufb03cient 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 di\ufb00ers from solutions proposed by network coding, we are able to provide insight into the tradeo\ufb00s and analysis of the problem of collaborative content distribution. We formally analyze the performance of the algorithm, demonstrating its e\ufb03ciency with high probability.<\/p>\n","protected":false},"excerpt":{"rendered":"
We study epidemic schemes in the context of collaborative data delivery. In this context, multiple chunks of data reside at di\ufb00erent 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. 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