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SM ISO690:2012 SCLIFOS, Alexei, SCLIFOS, Emilia, GUŢULEAC, Emilian. Performability Modeling of Self-Adaptive Systems Based on Extension Neural Rewriting Stochastic Petri Nets. In: Electronics, Communications and Computing, Ed. 12, 20-21 octombrie 2022, Chişinău. Chișinău: Tehnica-UTM, 2023, Editia 12, pp. 162-167. DOI: https://doi.org/10.52326/ic-ecco.2022/CS.03 |
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Electronics, Communications and Computing Editia 12, 2023 |
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Conferința "Electronics, Communications and Computing" 12, Chişinău, Moldova, 20-21 octombrie 2022 | ||||||
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DOI:https://doi.org/10.52326/ic-ecco.2022/CS.03 | ||||||
Pag. 162-167 | ||||||
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Traditional mathematical formalisms are unable to model modern self-adaptive discrete event systems (ADES) because they cannot handle behaviors that change at run-time in response to environmental changes. This paper introduces a new extension of Reconfigurable Stochastic reward Nets (RSRN), called Extension Neural Rewriting Petri Nets (ExNRPN), which enables the performability modeling and simulation of modern ADESs. ExNRPNs are obtained by incorporating in some special transitions of RSRNs an extension neural network (ENN) algorithm where the run-time calculation and reconfiguration is done in the local components, while the adaptation is performed for the whole system. The application of the proposed ExNRPN is illustrated by performability modeling a particular ADES. |
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Cuvinte-cheie adaptive system, extension neural network, performability modeling, rewriting rule, stochatic Petri net |
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