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Simulation parfaite de réseaux fermés de files d’attente et génération aléatoire de structures combinatoires

Abstract : Random generation of combinatorial objects is an important problem in many fields of research (communications networks, theoretical computing, combinatorics, statistical physics, ...). This often requires sampling the stationary distribution of an ergodic Markov chain. In 1996, Propp and Wilson introduced an algorithm to produce unbiased samples of the stationary distribution, also called a perfect sampling algorithm. It requires parallel simulation of all possible states of the chain. To avoid simulating all the trajectories, several strategies have been implemented. But they are related to the structure of the chain and require a monotonicity property, or a construction of a bounding chain that exploits the lattice structure of the state space or the local character of the transitions.In the field of communications networks, attention is paid to the performance of queueing networks, that can be distinguished into two groups: the networks that have a product form stationary distribution which is easy to compute. Random generation can be used for the others. Perfect sampling algorithms can be used for open queueing networks, thanks to the lattice structure of their state space. Unfortunately, that is not the case for closed queueing networks, due to the size of the state space which is exponential in the number of queues and a global constraint (a constant number of customers). The main contribution of this thesis is a new data structure called a diagram. It is inspired by dynamic programming and allows a new technique of construction of bounding processes. The first part of the manuscript is devoted to the implementation of the Propp and Wilson algorithm for closed queueing networks. The representation of a set of states by a diagram and the transition operation for the bounding process has a polynomial complexity in the number of queues and customers. This technique is extended to closed multi-class networks and to networks with synchronizations. Specification of sets of objects that can be represented by a diagram and generic algorithms that use this data structure are proposed in this manuscript. The Boltzmann method is another unbiased sampling technique. It is based on analytical combinatorics and produces uniform samples from objects that belong to the same combinatorial class. It is used in the second part of this thesis in order to sample the stationary distribution of closed networks with product form and for the generation of multisets of fixed cardinality. Diagrams are used again in this context. Finally, the third part presents the software produced during this thesis, implementing diagrams and perfect simulation of closed queueing networks.
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Submitted on : Tuesday, June 26, 2018 - 5:12:13 PM
Last modification on : Thursday, October 29, 2020 - 3:01:48 PM
Long-term archiving on: : Wednesday, September 26, 2018 - 10:16:15 PM


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  • HAL Id : tel-01824054, version 1



Christelle Rovetta. Simulation parfaite de réseaux fermés de files d’attente et génération aléatoire de structures combinatoires. Modélisation et simulation. Université Paris sciences et lettres, 2017. Français. ⟨NNT : 2017PSLEE051⟩. ⟨tel-01824054⟩



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