In this paper, the authors introduce the Quadratic Resolution Framework (QRF), a numerical modeling framework to evaluate complex stochastic queuing networks. They say that QRF offers a hierarchy of alternative approximation methods for the evaluation of complex stochastic networks, which includes both approximations and bounds. They present the methodology for the QRF approach and explain their modeling assumptions. The authors develop the analytical characterization of QRF models and the procedure by which QRF models are automatically mapped into a set of linear inequalities. They study the performance evaluation of QRF models and their applicability in comparison with simulation and exact methods.
Recensione dell'articolo:(Casale, Giuliano; De Nitto Personé, Vittoria; Smirni, Eugenia; - "QRF: an optimization-based framework for evaluating complex stochastic networks." - ACM Trans. Model. Comput. Simul. 26 (2016), no. 3) MR3458939 MathSciNet ISSN 2167-5163
Leonardo Pasini
2016-01-01
Abstract
In this paper, the authors introduce the Quadratic Resolution Framework (QRF), a numerical modeling framework to evaluate complex stochastic queuing networks. They say that QRF offers a hierarchy of alternative approximation methods for the evaluation of complex stochastic networks, which includes both approximations and bounds. They present the methodology for the QRF approach and explain their modeling assumptions. The authors develop the analytical characterization of QRF models and the procedure by which QRF models are automatically mapped into a set of linear inequalities. They study the performance evaluation of QRF models and their applicability in comparison with simulation and exact methods.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
