The increasing heterogeneity of the VM offerings on public IaaS clouds gives rise to a very large number of deployment options for constructing distributed, multi-component cloud applications. However, selecting an appropriate deployment variant , i.e., a valid combination of deployment options, to meet required performance levels is non-trivial. The combinatorial explosion of the deployment space makes it infeasible to measure the performance of all deployment variants to build a comprehensive empirical performance model. To address this problem, we propose Feature-Oriented Cloud (FOCloud), a performance engineering approach for deployment configurable cloud applications. FOCloud (i) uses feature modeling to structure and constrain the valid deployment space by modeling the commonalities and variations in the different deployment options and their inter-dependencies, (ii) uses sampling and machine learning to incrementally and cost-effectively build a performance prediction model whose input variables are the deployment options, and the output variable is the performance of the resulting deployment variant, and (iii) uses Explainable AI techniques to provide explanations for the prediction outcomes of valid deployment variants in terms of the deployment options. We demonstrate the practicality and feasibility of FOCloud by applying it to an extension of the RuBiS benchmark application deployed on Google Cloud.

中文翻译：

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FOCloud：特征模型引导的性能预测和部署可配置云应用的解释


公共 IaaS 云上的虚拟机产品的异构性不断增加，为构建分布式、多组件云应用程序提供了大量的部署选项。然而，选择适当的部署变体（即部署选项的有效组合）来满足所需的性能水平并非易事。部署空间的组合爆炸使得测量所有部署变体的性能以构建全面的经验性能模型变得不可行。为了解决这个问题，我们提出了面向功能的云（FOCloud），这是一种用于部署可配置云应用程序的性能工程方法。 FOCloud (i) 通过对不同部署选项及其相互依赖性的共性和变化进行建模，使用特征建模来构建和约束有效的部署空间，(ii) 使用采样和机器学习以增量且经济高效的方式构建性能预测该模型的输入变量是部署选项，输出变量是最终部署变体的性能，并且 (iii) 使用可解释的 AI 技术根据部署选项为有效部署变体的预测结果提供解释。我们通过将 FOCloud 应用于部署在 Google Cloud 上的 RubiS 基准应用程序的扩展来展示 FOCloud 的实用性和可行性。