Mean-field framework for performance evaluation of push-pull gossip protocols
Performance Evaluation
Semantic analysis of gossip protocols for wireless sensor networks
CONCUR'11 Proceedings of the 22nd international conference on Concurrency theory
Analysis of a clock synchronization protocol for wireless sensor networks
Theoretical Computer Science
Comparison of the mean-field approach and simulation in a peer-to-peer botnet case study
EPEW'11 Proceedings of the 8th European conference on Computer Performance Engineering
Analysis of gossip-based information propagation in wireless mesh networks
EPEW'11 Proceedings of the 8th European conference on Computer Performance Engineering
Availability in large networks: global characteristics from local unreliability properties
MMB'12/DFT'12 Proceedings of the 16th international GI/ITG conference on Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance
CONCUR'12 Proceedings of the 23rd international conference on Concurrency Theory
Continuous approximation of collective system behaviour: A tutorial
Performance Evaluation
Bounds on the deviation of discrete-time Markov chains from their mean-field model
Performance Evaluation
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Gossip protocols are designed to operate in very large, decentralised networks. A node in such a network bases its decision to interact (gossip) with another node on its partial view of the global system. Because of the size of these networks, analysis of gossip protocols is mostly done using simulations, that tend to be expensive in computation time and memory consumption. We employ mean-field approximation for an analytical evaluation of gossip protocols. Nodes in the network are represented by small identical stochastic models. Joining all nodes would result in an enormous stochastic process. If the number of nodes goes to infinity, however, mean-field analysis allows us to replace this intractably large stochastic process by a small deterministic process. This process approximates the behaviour of very large gossip networks, and can be evaluated using simple matrix-vector multiplications.