MeanField analysis for the evaluation of gossip protocols
ACM SIGMETRICS Performance Evaluation Review
Analysis of Large Populations of Interacting Objects with Mean Field and Markovian Agents
EPEW '09 Proceedings of the 6th European Performance Engineering Workshop on Computer Performance Engineering
Analysis of Television and Cinema Productions using Mean Field Models
Electronic Notes in Theoretical Computer Science (ENTCS)
Mean-field framework for performance evaluation of push-pull gossip protocols
Performance Evaluation
A mean field approach for optimization in discrete time
Discrete Event Dynamic Systems
Fluid computation of passage-time distributions in large Markov models
Theoretical Computer Science
Mean-field approximations for performance models with generally-timed transitions
ACM SIGMETRICS Performance Evaluation Review
Higher moment analysis of a spatial stochastic process algebra
EPEW'11 Proceedings of the 8th European conference on Computer Performance Engineering
Fluid limits of queueing networks with batches
ICPE '12 Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering
CONCUR'12 Proceedings of the 23rd international conference on Concurrency Theory
Continuous approximation of collective system behaviour: A tutorial
Performance Evaluation
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Modeling and analysing very large stochastic systems composed of interacting entities is a very challenging and complex task. The usual approach, relying on the generation of the whole state space, is bounded by the state space explosion, even if symmetry properties, often included in the model, allow to apply lumping techniques and building the overall model by means of tensor algebra operations. In this paper we resort to the mean field theory. The main idea of the mean field theory is to focus on one particular tagged entity and to replace all interactions with the other entities with an average or effective interaction. The reduction of a multibody problem into an effective one-body problem makes the solution easier while at the same time taking into account the contribution of an averaged interdependence of the whole system on the specific entity. We apply the mean field approach to very large systems of interacting continuous time Markov chains, in which the averaged interaction depends on the distribution of the entity population in each state. We report several examples of interacting Markovian queues, showing the potentialities of the proposed technique.