Epidemic algorithms for replicated database maintenance
PODC '87 Proceedings of the sixth annual ACM Symposium on Principles of distributed computing
Gossip-Based Computation of Aggregate Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
Epidemic-Style Proactive Aggregation in Large Overlay Networks
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Decentralized Schemes for Size Estimation in Large and Dynamic Groups
NCA '05 Proceedings of the Fourth IEEE International Symposium on Network Computing and Applications
Peer counting and sampling in overlay networks: random walk methods
Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Distributed estimation of global parameters in delay-tolerant networks
Computer Communications
Decentralized monitoring in peer-to-peer systems
Benchmarking Peer-to-Peer Systems
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We propose Gossipico, a gossip algorithm to average, sum or find minima and maxima over node values in a large, distributed, and dynamic network. Unlike previous work, Gossipico provides a continuous estimate of, for example, the number of nodes, even when the network becomes disconnected. Gossipico converges quickly due to the introduction of a beacon mechanism that directs messages to an autonomously selected beacon node. The information spread through the network shows a percolation-like phase-transition and allows information to propagate along near-shortest paths. Simulations in various different network topologies (ranging in size up to one million nodes) illustrate Gossipico's robustness against network changes and display a near-optimal count time. Moreover, in a comparison with other related gossip algorithms, Gossipico displays an improved and more stable performance over various classes of networks.