Modeling parameter and context dependencies in online architecture-level performance models
Modeling parameter and context dependencies in online architecture-level performance models
复制标题
在线架构级性能模型中对参数和上下文依赖关系进行建模
DOI：
--
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Samuel Kounev
中科院分区：
文献类型：
--
作者：
Fabian Brosig;Nikolaus Huber;Samuel Kounev
现代面向服务的企业系统具有越来越复杂和动态的松散耦合体系结构，通常表现出较差的性能和资源效率，并且运营成本高。这是由于无法在运行时预测系统环境中动态变化的影响并相应地调整系统配置。体系结构级的性能模型为性能预测提供了强大的工具，但是，当前对软件组件的执行上下文建模的方法不适合在运行时使用。在本文中，我们分析了典型的在线绩效预测方案，并提出了一种新型的性能元模型，用于表达和解决参数和上下文依赖关系，专门设计用于在线场景中。我们在基于SpecJenterPrise2010标准基准的现实和代表性的在线绩效预测方案的背景下激励和验证我们的方法。
Modern service-oriented enterprise systems have increasingly complex and dynamic loosely-coupled architectures that often exhibit poor performance and resource efficiency and have high operating costs. This is due to the inability to predict at run-time the effect of dynamic changes in the system environment and adapt the system configuration accordingly. Architecture-level performance models provide a powerful tool for performance prediction, however, current approaches to modeling the execution context of software components are not suitable for use at run-time. In this paper, we analyze the typical online performance prediction scenarios and propose a novel performance meta-model for expressing and resolving parameter and context dependencies, specifically designed for use in online scenarios. We motivate and validate our approach in the context of a realistic and representative online performance prediction scenario based on the SPECjEnterprise2010 standard benchmark.