We present a new method for the accurate analysis of the quality-of-service (QoS) properties of component-based systems. Our method takes as input a QoS property of interest and a high-level continuous-time Markov chain (CTMC) model of the analysed system, and refines this CTMC based on observations of the execution times of the system components. The refined CTMC can then be analysed with existing probabilistic model checkers to accurately predict the value of the QoS property. The paper describes the theoretical foundation underlying this model refinement, the tool we developed to automate it, and two case studies that apply our QoS analysis method to a service-based system implemented using public web services and to an IT support system at a large university, respectively. Our experiments show that traditional CTMC-based QoS analysis can produce highly inaccurate results and may lead to invalid engineering and business decisions. In contrast, our new method reduced QoS analysis errors by 84.4–89.6 percent for the service-based system and by 94.7–97 percent for the IT support system, significantly lowering the risk of such invalid decisions.

中文翻译：

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基于组件的系统的观察增强 QoS 分析

我们提出了一种准确分析基于组件的系统的服务质量 (QoS) 属性的新方法。我们的方法将感兴趣的 QoS 属性和分析系统的高级连续时间马尔可夫链 (CTMC) 模型作为输入，并根据对系统组件执行时间的观察来改进该 CTMC。然后可以使用现有的概率模型检查器分析改进后的 CTMC，以准确预测 QoS 属性的值。本文描述了此模型改进的理论基础、我们开发的自动化工具，以及将我们的 QoS 分析方法应用于使用公共 Web 服务实施的基于服务的系统和大型大学的 IT 支持系统的两个案例研究， 分别。我们的实验表明，传统的基于 CTMC 的 QoS 分析会产生高度不准确的结果，并可能导致无效的工程和业务决策。相比之下，我们的新方法将基于服务的系统的 QoS 分析错误减少了 84.4-89.6%，将 IT 支持系统的 QoS 分析错误减少了 94.7-97%，显着降低了此类无效决策的风险。