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首页> 外文会议>IEEE Pacific Rim International Symposium on Dependable Computing >Mercury: Performance and Dependability Evaluation of Systems with Exponential, Expolynomial, and General Distributions
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Mercury: Performance and Dependability Evaluation of Systems with Exponential, Expolynomial, and General Distributions

机译：汞：具有指数分布，指数分布和一般分布的系统的性能和可靠性评估

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摘要

The evaluation of dependability or performance of general systems usually relies on the assistance of stochastic modeling and simulation tools. Those software packages enables the creation of models and computation of metrics quickly and accurately. This paper introduces the Mercury tool, which is an integrated software that enables creating and evaluating Reliability Block Diagrams, Stochastic Petri Nets, Continuous Time Markov Chains, and Energy Flow Models. Mercury provides a graphical user interface, a script language for command-line interface, and also an API (Application Programming Interface) that enables interaction through external applications. The evaluation of models is not restricted to the assumption of Exponential distributions, which is a common constraint in other similar tools. Mercury implements a simulation framework that allows more than 25 probability distributions, as well as a moment matching method that enables expolynomial —phase-type —distributions for models solved through numerical analysis. This paper presents the main features and methods available in Mercury to aid the dependability and performance evaluation of various systems, for both academy and industry. The accuracy and applicability of the tool is illustrated by a case study of packet loss and throughput for a Video on Demand (VoD) system.
机译：一般系统的可靠性或性能评估通常依赖于随机建模和仿真工具的协助。这些软件包可以快速，准确地创建模型和度量计算。本文介绍了Mercury工具，它是一个集成软件，可用于创建和评估可靠性框图，随机Petri网，连续时间马尔可夫链和能流模型。 Mercury提供了图形用户界面，用于命令行界面的脚本语言，以及一个API（应用程序编程接口），该API支持通过外部应用程序进行交互。模型的评估不仅限于指数分布的假设，这是其他类似工具中的常见约束。 Mercury实现了允许超过25种概率分布的仿真框架，以及为通过数值分析解决的模型实现了多项式（相位类型）分布的矩匹配方法。本文介绍了Mercury中可用的主要特征和方法，以帮助学术界和工业界对各种系统进行可靠性和性能评估。通过视频点播（VoD）系统的数据包丢失和吞吐量的案例研究，说明了该工具的准确性和适用性。

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