Performance management in component-oriented systems using a Model Driven Architecture/spl trade/ approach
Performance management in component-oriented systems using a Model Driven Architecture/spl trade/ approach
复制标题
使用模型驱动架构/spl trade/方法在面向组件的系统中进行性能管理
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
John Murphy
中科院分区：
文献类型：
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作者：
Adrian Mos;John Murphy
开发人员通常缺乏时间或知识来深刻理解大型面向组件的企业应用程序中的性能问题。由于此类应用程序通常使用内部组件和商业现成(COTS)组件的混合构建，这一情况变得更加复杂。本文提出了一种方法，用于理解和预测面向组件的分布式系统在开发过程中和构建后的性能。该方法以三个概念上独立的部分为基础：监测、建模和业绩预测。性能预测基于通过监视和分析活动的或正在开发的系统动态创建的UML模型。系统使用非侵入性方法进行监控，并收集运行时数据。此外，通过分析目标应用程序的部署配置来获得静态数据。增强了性能指标的UML模型是基于静态和动态数据创建的，显示了性能热点。为了便于理解系统，生成的模型既可以在事务之间的同一抽象级别水平遍历，也可以使用模型驱动体系结构定义的概念在不同的抽象层之间垂直遍历。通过生成工作负载和模拟性能模型，在不同的场景中预测系统性能并识别与性能相关的问题。目前正在为所提出的方法实现一个框架，目前的重点是企业Java Beans技术。
Developers often lack the time or knowledge to profoundly understand the performance issues in largescale component-oriented enterprise applications. This situation is further complicated by the fact that such applications are often built using a mix of in-house and commercial-off-the-shelf (COTS) components. This paper presents a methodology for understanding and predicting the performance of component-oriented distributed systems both during development and after they have been built. The methodology is based on three conceptually separate parts: monitoring, modelling and performance prediction. Performance predictions are based on UML models created dynamically by monitoring-and-analysing a live or under-development system. The system is monitored using non-intrusive methods and run-time data is collected. In addition, static data is obtained by analysing the deployment configuration of the target application. UML models enhanced with performance indicators are created based on both static and dynamic data, showing performance hot spots. To facilitate the understanding of the system, the generated models are traversable both horizontally at the same abstraction level between transactions, and vertically between different layers of abstraction using the concepts defined by the Model Driven Architecture. The system performance is predicted and performance-related issues are identified in different scenarios by generating workloads and simulating the performance models. Work is under way to implement a framework for the presented methodology with the current focus on the Enterprise Java Beans technology.