Markov Nets: Probabilistic Models for Distributed and Concurrent Systems.

Albert Benveniste 1 Eric Fabre 1 Stefan Haar 1
1 SIGMA2 - Signal, models, algorithms
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, INRIA Rennes
Abstract : For distributed systems, i.e., large complex networked systems, there is a drastic difference between a local view and knowledge of the system, and its global view. Distributed systems have local state and time, but do not possess global state and time in the usual sense. In this paper, motivated by the monitoring of distributed systems and in particular of telecommunications networks, we develop a generalization of Markov chains and hidden Markov models for distributed and concurrent systems. By a concurrent system, we mean a system in which components may evolve independently, with sparse synchronizations. We follow a so-called true concurrency approach, in which neither global state nor global time are available. Instead, we use only local states in combination with a partial order model of time. Our basic mathematical tool is that of Petri net unfoldings.
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IEEE Transactions on Automatic Control, Institute of Electrical and Electronics Engineers, 2003, 48 (11), pp.1936-1950. 〈10.1109/TAC.2003.819076〉
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Albert Benveniste, Eric Fabre, Stefan Haar. Markov Nets: Probabilistic Models for Distributed and Concurrent Systems.. IEEE Transactions on Automatic Control, Institute of Electrical and Electronics Engineers, 2003, 48 (11), pp.1936-1950. 〈10.1109/TAC.2003.819076〉. 〈inria-00638221〉

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