Blocking is the phenomenon where a service request is momentarily stopped, but not lost, until the service becomes available again. Despite its importance, blocking is a difficult phenomenon to model analytically, because it creates strong interdependencies in the systems components. Mean Value Analysis (MVA) is one of the most appealing evaluation methodology due to its low computational cost and easy of use. In this report, an approximate MVA for Bloking After Service is presented that greatly outperforms previous results. The new algorithm, called MVABAS v2, is obtained by analyzing the inter-dependencies due to the blocking mechanism and by consequently modifying the MVA equations. Since its simplicity and easy of use, the proposed approach is particularly suitable for non-expert people. This report presents results obtained by applying MVABAS v2 over about 60 networks opportunely chosen to stress the most critical aspects of the proposed approximation. Moreover, the report shows parameters derivation for the method application to a case study in the field of healthcare.
DE NITTO PERSONE', V., Andrea Di Lonardo, (2018). MVABAS v2: experimentation networks and results [Rapporto tecnico].
MVABAS v2: experimentation networks and results
Vittoria de Nitto Personè
;
2018-05-02
Abstract
Blocking is the phenomenon where a service request is momentarily stopped, but not lost, until the service becomes available again. Despite its importance, blocking is a difficult phenomenon to model analytically, because it creates strong interdependencies in the systems components. Mean Value Analysis (MVA) is one of the most appealing evaluation methodology due to its low computational cost and easy of use. In this report, an approximate MVA for Bloking After Service is presented that greatly outperforms previous results. The new algorithm, called MVABAS v2, is obtained by analyzing the inter-dependencies due to the blocking mechanism and by consequently modifying the MVA equations. Since its simplicity and easy of use, the proposed approach is particularly suitable for non-expert people. This report presents results obtained by applying MVABAS v2 over about 60 networks opportunely chosen to stress the most critical aspects of the proposed approximation. Moreover, the report shows parameters derivation for the method application to a case study in the field of healthcare.File | Dimensione | Formato | |
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