SIAM Journal on Applied Mathematics
Epidemic algorithms for replicated database maintenance
PODC '87 Proceedings of the sixth annual ACM Symposium on Principles of distributed computing
Accessing nearby copies of replicated objects in a distributed environment
Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures
Spatial gossip and resource location protocols
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Protocols and Impossibility Results for Gossip-Based Communication Mechanisms
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
Randomized Broadcast in Networks
SIGAL '90 Proceedings of the International Symposium on Algorithms
Efficient Epidemic-Style Protocols for Reliable and Scalable Multicast
SRDS '02 Proceedings of the 21st IEEE Symposium on Reliable Distributed Systems
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Universal augmentation schemes for network navigability: overcoming the √n-barrier
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
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Gossip protocols are communication protocols in which, periodically, every node of a network exchanges information with some other node chosen according to some (randomized) strategy. These protocols have recently found various types of applications for the management of distributed systems. Spatial gossip protocols are gossip protocols that use the underlying spatial structure of the network, in particular for achieving the ”closest-first” property. This latter property states that the closer a node is to the source of a message the more likely it is to receive this message within a prescribed amount of time. Spatial gossip protocols find many applications, including the propagation of alarms in sensor networks, and the location of resources in P2P networks. We design a sub-linear spatial gossip protocol for arbitrary graphs metric. More specifically, we prove that, for any graph metric with maximum degree Δ, for any source s and any ball centered at s with size b, new information is spread from s to all nodes in the ball within $O( (\sqrt {b \log b}\, \log \log b + \Delta) \log b )$ rounds, with high probability. Moreover, when applied to general metrics with uniform density, the same protocol achieves a propagation time of O(log2bloglogb) rounds.