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Scalable Differential Analysis of Process Algebra Models

机译:过程代数模型的可扩展微分分析

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The exact performance analysis of large-scale software systems with discrete-state approaches is difficult because of the well-known problem of state-space explosion. This paper considers this problem with regard to the stochastic process algebra PEPA, presenting a deterministic approximation to the underlying Markov chain model based on ordinary differential equations. The accuracy of the approximation is assessed by means of a substantial case study of a distributed multithreaded application.
机译:由于众所周知的状态空间爆炸问题,使用离散状态方法对大型软件系统进行精确的性能分析非常困难。本文考虑了随机过程代数PEPA的问题,提出了基于常微分方程的底层马尔可夫链模型的确定性近似。通过对分布式多线程应用程序的大量案例研究来评估近似的准确性。

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