G-networks with propagating resets via RCAT PG. Harrison Dept. of Computing Imperial College London South Kensington Campus, London SW7 2AZ ABSTRACT Stationary Markovian networks, defined by a collection of cooperating agents, can be solved for their equilibrium state probability distribution by a new compositional method that computes their reversed Markov process, under appropriate conditions . We apply this approach to G-networks with chains of triggers and generalised resets, which have some quite distinct properties from the resets proposed recently. Using the Reversed Compound Agent Theorem (RCAT) of [5], we find the reversed process of the continuous time Markov chain describing G-networks with positive and negative triggers and a new form of resets [2, 3] . From the reversed process, we immediately obtain the product-form solution for the equilibrium state probabilities of the networks . The resets we define, like negative triggers [1], can propagate through a sequence of nodes causing local state transitions at each. This is not the case with Gelenbe and Fourneau's resets [4] and a comparison is made between the two notions . For reference, the statement of RCAT is given in the Appendix . 1. INTRODUCTION tomer removals at several nodes ensues, terminating when
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G-networks with propagating resets via RCAT
Harrison, P. G.
Follow ACM SIGMETRICS Performance Evaluation Review
, Volume 31 (2)
Association for Computing Machinery – Sep 1, 2003
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