computing

Abstract: The optimal resource allocation to satisfy such demands and the proper settlement of contention when demands exceed the capacity of the resources, constitute the problem of being able to understand and to predict system behavior. To this analysis we can use both analytical and simulation methods. Modeling and simulation are methods, which are commonly used by performance analysts to represent constraints and to optimize performance. Principally analytical methods represented first of all by queuing theory belongs to the preferred method in comparison to the simulation method, because of their potential ability of general analysis and also of their ability to potentially analyze also massive parallel computers. But these arguments supposed to develop and to verify suggested analytical models. This article goes further in applying the achieved analytical results in queuing theory for complex performance evaluation in parallel computing [9, 14]. The extensions are mainly in extending derived analytical models to whole range of parallel computers including massive parallel computers (Grid, meta computer). The article therefore describes standard analytical model based on M/M/m, M/D/m and M/M/1, M/D/1 queuing theory systems. Then the paper describes derivation of the correction factor for standard analytical model, based on M/M/m and M/M/1 queuing systems, to study more precise their basic performance parameters (overhead latencies, throughput etc.). All the derived analytical models were compared with performed simulation results in order to estimate the magnitude of improvement. Likewise they were tested under various ranges of parameters, which influence th