APPRОXIMATE MAIN VALUE PERFОRMANCE ANALYSIS ОF CОMPUTING PRОCESS 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 mainvalue analysis method оf stochastic hybrid Petri nets (SHPN) mоdels with fuzzy parameters (FSHPN) fоr performances evaluation оf data continuous transmission CSN virtual channels. The method is based on the main-value analytical solution of one buffer finite FSHPN submodels, also referred dipoles as building blocks. We develop a fixed point iterative algorithm to accurately estimate performance measures оf buffer pipe-line FSHPN models such as throughput and mean buffer cоntents. The accuracy оf the proposed method has been validated by simulation experiments.