将监控数据转换为性能模型的通用平台

Jonas Kunz, Christoph Heger, R. Heinrich
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引用次数: 3

摘要

软件系统的性能是业界持续关注的问题,包括相应性能模型的开发。最近,人们提出了几种从监测数据中推导此类性能模型的方法。这些方法目前的一个限制是,它们中的大多数都绑定到提供数据的某些监测工具,限制了它们的适用性。因此,我们提出了一个通用平台,用于将监控数据转换为性能模型,封装这些方法以获得性能模型。该平台提供了交换监视工具或使用的性能建模方法的灵活性,允许更全面的性能分析,而无需额外的手动转换工作。性能建模方法的无缝可交换性支持基于相同的监视数据生成不同类型的性能模型,而监视工具的可交换性支持在更广泛的系统上使用相同的方法,因为某些监视工具的适用性通常受到环境属性的限制。此外,该平台的通用特性旨在支持在基于监控数据的性能建模上下文中快速开发新的、即将出现的想法的原型。在评估过程中,我们从准确性和可扩展性的角度检查我们的方法的质量。我们展示了用于将监视数据转换为性能模型的平台的开销非常低,并且与非集成版本的结果相比,集成性能建模方法的结果非常准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Generic Platform for Transforming Monitoring Data into Performance Models
The performance of software systems is an ongoing issue in the industry, including the development of corresponding performance models. Recently several approaches for deriving such performance models from monitoring data have been proposed. A current limitation of these approaches is that most of them are bound to certain monitoring tools for providing the data, limiting their applicability. We therefore propose a generic platform for transforming monitoring data into performance models, encapsulating these approaches for deriving performance models. This platform gives the flexibility of exchanging the monitoring tool or the used performance modeling approach, allowing more comprehensive performance analysis without additional manual transformation work. A seamless exchangeability of the performance modeling approach enables the generation of different types of performance models based on the same monitoring data, while the exchangeability of the monitoring tool enables the same approaches to be employed on a wider range of systems, as often the applicability of certain monitoring tools is limited by environmental properties. In addition, the generic nature of the platform aims to support the rapid development of prototypes of new, upcoming ideas within the context of performance modeling based on monitoring data. During our evaluation we examine the quality of our approach in terms of accuracy and scalability. We show that our platform for transforming monitoring data into performance models scales with a very low overhead and that the results of the integrated performance modeling approaches are very accurate in comparison to the results of the non-integrated versions.
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