Topology Aware Gossip Overlays

Gossip, or epidemic, protocols have emerged as a highly scalable and resilient approach to implement several application level services such as reliable multicast, data aggregation, publish-subscribe, among others. All these protocols organize nodes in an unstructured random overlay network. In many cases, it is interesting to bias the random overlay such that it better matches the underlying network topology, for instance to reduce the stretch in the overlay routing. In this paper we propose a new scheme that allows an unstructured gossip overlay network to bias its topology accordingly to some efficiency metric, for instance to better match the topology of the underlying network. Our scheme is completely decentralized and, unlike previous approaches, preserves a number of interesting properties of the original (non-biased) overlay, such as the node degree, small diameter, and low clustering.