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Size Expansions of Mean Field Approximation: Transient and Steady-State Analysis

Abstract : Mean field approximation is a powerful tool to study the performance of large stochastic systems that is known to be exact as the system's size N goes to infinity. Recently, it has been shown that, when one wants to compute expected performance metric in steady-state, mean field approximation can be made more accurate by adding a term in 1/N to the original approximation. This is called the refined mean field approximation in [7]. In this paper, we show how to obtain the same result for the transient regime and we provide a further refinement by expanding the term in 1/N 2 (both for transient and steady-state regime). Our derivations are inspired by moment-closure approximation. We provide a number of examples that show this new approximation is usable in practice for systems with up to a few tens of dimensions.
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Submitted on : Tuesday, October 9, 2018 - 6:02:59 PM
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Nicolas Gast, Luca Bortolussi, Mirco Tribastone. Size Expansions of Mean Field Approximation: Transient and Steady-State Analysis. 2018 - 36th International Symposium on Computer Performance, Modeling, Measurements and Evaluation, Dec 2018, Toulouse, France. pp.1-2, ⟨10.1016/j.peva.2018.09.005⟩. ⟨hal-01891636⟩



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