APPROXIMATE MAIN VALUE PERFORMANCE ANALYSIS ОF COMPUTING PROCESS USING SHPN WITH FUZZY PARAMETERS

Stochastic fluid models are a class of analytic models that have recently drawn the attention of many researchers for the modeling and performance evaluation of complex computer systems and networks (CSN). In this paper we present an approximated main-value analysis method of stochastic hybrid Petri nets (SHPN) models with fuzzy parameters (FSHPN) for performances evaluation of data continuous transmission CSN virtual channels. The method is based on the main-value analytical solution of one buffer finite FSHPN sub-models, also referred dipoles as building blocks. We develop a fixed point iterative algorithm to accurately estimate performance measures of buffer pipe-line FSHPN models such as throughput and mean buffer contents. The accuracy of the proposed method has been validated by simulation experiments.