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Approximate throughput computation of stochastic marked graphs

机译:随机标记图的近似吞吐量计算

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摘要

A general iterative technique for approximate throughput computation of stochastic strongly connected marked graphs is presented. It generalizes a previous technique based on net decomposition through a single input-single output cut, allowing the split of the model through any cut. The approach has two basic foundations. First, a deep understanding of the qualitative behavior of marked graphs leads to a general decomposition technique. Second, after the decomposition phase, an iterative response time approximation method is applied for the computation of the throughput. Experimental results on several examples generally have an error of less than 3%. The state space is usually reduced by more than one order of magnitude; therefore, the analysis of otherwise intractable systems is possible.
机译:提出了一种通用的迭代技术,用于计算随机强连接标记图的吞吐量。它概括了基于通过单个输入-单个输出切割的净分解的先前技术,从而允许通过任何切割拆分模型。该方法有两个基本基础。首先,对标记图的定性行为的深刻理解导致了一种通用的分解技术。其次,在分解阶段之后,将迭代响应时间近似方法应用于吞吐量的计算。几个示例的实验结果的误差通常小于3%。状态空间通常会减少一个以上的数量级。因此,对原本棘手的系统进行分析是可能的。

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