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PNiQ-generalized stochastic petri nets including queuing networks

机译:PNIQ广义随机培养网,包括排队网络

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We combine generalized stochastic Petri nets (GSPN) and queuing networks at the modeling level by defining Petri Nets including Queuing Nets(PNiQ).The definition is especially designed to allow approximate analysis by aggregation of the queuing nets and replacing them with GSPN elements. Usually the aggregation of combined GSPN and queuing net models is carried out manually which limits the use of this technique to experts and furthermore may easily laead to modeling errors and larger approximation errors than inherent in the method This is what we want to avoid by the definition fo PNiQ which incorporate queuing networks into GSPN and provide interfaces between them.Thereby combined modeling is made easier and less error-prone even for the non-expert.Steady state analysis of the model can be carried out automatically: queuing net parts are analyzed with efficient queuing net-algorithms for large nets and replaced by GSPN subnets that model the delay of tokens in the queuing nets.The resulting GSPN can then be analyzed with state-of-the-art methods and tools.
机译:通过定义培养网(PNIQ)定义Petri网(PNIQ),将广义随机Petri网(GSPN)和排队网络组合在建模级别。特别设计的定义尤其旨在允许通过队列网聚合并用GSPN元素替换它们来近似分析。通常,GSPN组合和排队净模型的聚合是手动执行的,这限制了这种技术对专家的使用,此外,可以容易地缩放到建模错误和比该方法中固有的更大近似值误差,这是我们想要避免的定义将排队网络与GSPN合并到GSPN中并在它们之间提供接口。即使对于非专业,也可以更容易且更容易出错,更容易出错。可以自动执行模型的steady状态分析:分析了排队网零件用于大型网的高效Queuing Net算法,并由GSPN子网替换,该子网模型在排队网中模拟令牌的延迟。然后可以通过最先进的方法和工具分析所得到的GSPN。

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