Resource demands are crucial parameters for modeling and predicting the performance of software systems. Currently, resource demand estimators are usually executed once for system analysis. However, the monitored system, as well as the resource demand itself, are subject to constant change in runtime environments. These changes additionally impact the applicability, the required parametrization as well as the resulting accuracy of individual estimation approaches. Over time, this leads to invalid or outdated estimates, which in turn negatively influence the decision-making of adaptive systems. In this article, we present SARDE , a framework for self-adaptive resource demand estimation in continuous environments. SARDE dynamically and continuously tunes, selects, and executes an ensemble of resource demand estimation approaches to adapt to changes in the environment. This creates an autonomous and unsupervised ensemble estimation technique, providing reliable resource demand estimations in dynamic environments. We evaluate SARDE using two realistic datasets. One set of different micro-benchmarks reflecting different possible system states and one dataset consisting of a continuously running application in a changing environment. Our results show that by continuously applying online optimization, selection and estimation, SARDE is able to efficiently adapt to the online trace and reduce the model error using the resulting ensemble technique.

中文翻译：

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萨德

资源需求是建模和预测软件系统性能的关键参数。目前，资源需求估计器通常执行一次以进行系统分析。但是，受监控的系统以及资源需求本身会在运行时环境中不断变化。这些变化还会影响适用性、所需的参数化以及单个估计方法的最终准确性。随着时间的推移，这会导致无效或过时的估计，进而对自适应系统的决策产生负面影响。在这篇文章中，我们介绍萨德，一个在连续环境中进行自适应资源需求估计的框架。萨德动态和连续地调整、选择和执行一组资源需求估计方法，以适应环境的变化。这创建了一种自主和无监督的集成估计技术，在动态环境中提供可靠的资源需求估计。我们评估萨德使用两个真实的数据集。一组反映不同可能系统状态的不同微基准和一个由在不断变化的环境中连续运行的应用程序组成的数据集。我们的结果表明，通过不断应用在线优化、选择和估计，萨德能够有效地适应在线跟踪并使用生成的集成技术减少模型误差。