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Performance analysis of real-time rewriting models

机译:实时重写模型的性能分析

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Real-time systems usually involve a subtle interaction of a number of distributed components and have a high degree of parallelism, which makes their performance analysis quite complex. Thus, traditional techniques, such as simulation, fail to produce reasonable results. Formal methods pose an interesting solution but they usually lack the capabilities to reason about quantitative time and probabilistic properties, which play a vital role in performance analysis. This paper addresses this issue by presenting a formal approach for assessing the performance of a real-time system. To describe the evolution of the system, we use a real-time rewriting logic, in which we mechanize the extraction of quantitative information from a timed model. To evaluate the performance, we first consider the set of runs obtained from different initial input values that are not equivalent modulo the equational theory associated with the model. The overall performance of the system is then evaluated as the performance of each run weighted by its probability mass function. In order to illustrate the practical effectiveness of the proposed approach, we present the formal modeling and performance analysis of a simple search engine.
机译:实时系统通常涉及许多分布式组件的微妙交互,并具有高度的并行性,这使得它们的性能分析相当复杂。因此,诸如仿真之类的传统技术无法产生合理的结果。形式化方法构成了一个有趣的解决方案,但它们通常缺乏推理时间和概率特性的能力,而这在性能分析中起着至关重要的作用。本文通过提出一种评估实时系统性能的正式方法来解决此问题。为了描述系统的发展,我们使用了实时重写逻辑,在其中我们机械化了从定时模型中提取定量信息的过程。为了评估性能,我们首先考虑从不同初始输入值获得的一组运行,这些初始输入值与该模型相关联的等式理论的模数不等价。然后,将系统的整体性能评估为每次运行的性能,并通过其概率质量函数对其进行加权。为了说明所提出方法的实际有效性,我们介绍了一个简单搜索引擎的形式化建模和性能分析。

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