As a consequence, exhaustive search of the design space is prohibitive, and more sophisticated techniques have to be used to find “good” solutions. Further, the space of possible solutions is normally very large, i.e., many design alternatives exist.
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In addition, the choices affect several design goals, the alternatives therefore represent a multi-criteria decision problem. The decisions are located at various levels of abstraction. During several steps in a state-of-the-art design flow, designers have to decide between many design alternatives. Moreover, a methodology to obtain such a parametric Petri net, called compound Petri net, from an easier-to-obtain set of alternative Petri nets is proposed and an application example is given.ĭesign space exploration is an important factor in embedded systems design. In this paper, a review of some definitions of parametric Petri net found in the literature is presented, as well as a definition for the general framework of stating optimization problems of both, the operation and the design of the model of the DES.
#Timenet app manual#
A manual choice of a reduced set of configurations to be simulated can be improved by the use of parametric Petri nets and a metaheuristic search of the most promising ones. An important drawback is the significant computational resources required to perform an exhaustive exploration of the state space due to the combinatorial explosion. Their main advantage consists of being applicable to most of the systems. A large range of approaches based on the simulation of the behavior of the system have been reported to answer this problem. The decision making processes that arise in the design and operation of this kind of systems can be afforded by means of algorithmic methodologies. Many technological, industrial or economical systems are described by discrete event system (DES) models. alphaFactory can be easily integrated into other existing tools, and we show its integration inside the GreatSPN framework, to solve Markov Regenerative Stochastic Petri Nets.
#Timenet app series#
Truncation of the infinite series of alpha-factors is determined by a novel error bound, which provides a reliable truncation point also in case of defective PDFs.
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The main goal of alphaFactory is to provide a freely available implementation for the computation of alpha-factors, to be used inside any extended Uniformization method implementation. This tool paper describes alphaFactory, a tool that computes the series of alpha-factors of a general distribution function starting from f(x). The extended Uniformization does not manipulate directly the distribution, as the whole computation is based on the alpha-factors of f(x), and the maximum CTMC rate \(\mu \). Usually, f(x) is taken as the deterministic distribution, leading to the computation of the CTMC probability at time t, but Uniformization may be extended to use other distributions.
#Timenet app pdf#
The Uniformization method computes the probability distribution of a CTMC of maximum rate \(\mu \) at the time a general event with PDF f(x) fires.