Introduction Stochastic Petri net based Markov modeling is a potentially very powerful and generic approach for evaluating the performance and depend ability of many di erent systems such as computer systems communication networks manufacturing sys tems etc As a consequence of their general appli cability SPN based Markov models form the basic solution approach for several software packages tha...

Markovian queueing networks may be modeled by generalized stochastic Petri nets. The simulation of a model in a properly chosen simulator provides information on the performances of the actual system, mainly in case that the analytical study is intricate. After presenting some general notions on queueing networks and on generalized stochastic Petri nets, this paper points out the PN Toolbox for...

Petri nets represent a modeling paradigm which allows to build general models by using a small number of graphical primitives. The strong point of Petri nets lies in their ability to model system aspects such as concurrency and synchronization and to represent these aspects graphically. Due to this reason, stochastic Petri nets (SPNs) are frequently used for model-based performance and dependab...

this paper deals with the problem of forbidden states in discrete event systems modeled by petri net. to avoid the forbidden states, some constraints which are called generalized mutual exclusion constraints can be assigned to them. enforcing these constraints on the system can be performed using control places. however, when the number of these constraints is large, a large number of control p...

The Stochastic Petri Net Package (SPNP) [2] is a versatile modeling tool for solution of Stochastic Petri Net (SPN) models. The SPN models are described in the input language for SPNP called CSPL (C-based SPN Language) which is an extension of the C programming language [8] with additional constructs which facilitate easy description of SPN models. Moreover, if the user does not want to describ...

in this paper, intelligent algorithm of ALATO based on Learning Automata using Adaptive Stochastic Petri Net presented to optimize the time in Economical Grid. Adaptive Stochastic Petri nets based on learning automata predicts the following optimized state through getting information from former system states and dynamic environmental reactions, makes the system current states appeared based on...

Performance analysis can be carried out in several ways, especially in case of Markovian models. In order to interpret high level of abstraction, we often use modeling tools like timed Petri nets (TPNs). Although some subclasses of TPNs (e.g. stochastic Petri nets (SPNs) [17, 19]) can be handled analytically, a general timed Petri net is hard to evaluate via numerical analysis. However, the sim...

In order to assist the performance evaluation of complex stochastic models, automatic program tools are developed since a long time. An effective model description language, supported by several software tools, is the stochastic Petri Net (SPN). But the attention to the analytical description and the numerical analysis of non-Markovian stochastic Petri Net models arose only recently. There are ...

Failures are very often affected by many other factors besides the time. In reality, very often, these factors are neglected, leading to misleading conclusions about reliability of stochastic systems. In this paper we identify the key factors that contribute to failure rates of various stochastic systems. We further formalize them and provide solutions for their modeling using stochastic Petri ...