论文标题
SLO-ML：多云应用程序中的服务级别目标建模语言
SLO-ML: A Language for Service Level Objective Modelling in Multi-cloud Applications
论文作者
论文摘要
云建模语言（CML）旨在帮助客户解决云市场中服务的多样性。尽管文献中已经提出了许多CML，但它们缺乏根据客户应用程序的特定服务水平目标自动选择服务的实际支持。我们提出了SLO-ML，这是一种小说和生成的CML，以捕获服务水平的要求，然后选择服务以尊重客户要求并生成适合这些服务的部署代码。我们介绍了SLO-ML的建筑设计和实现部署操作的相关经纪人。我们使用混合方法方法严格评估SLO-ML。首先，我们使用现实世界中的云应用程序与一组研究人员和开发人员进行实验案例研究。我们还通过详尽的经验可伸缩性测试评估开销。通过表达获得生产率和可用性的水平，我们突出了SLO-ML在使以用户为中心的云经纪人的巨大潜力。随着应用程序要求的增长，我们还讨论了限制。
Cloud modelling languages (CMLs) are designed to assist customers in tackling the diversity of services in the cloud market. While many CMLs have been proposed in the literature, they lack practical support for automating the selection of services based on the specific service level objectives of a customer's application. We put forward SLO-ML, a novel and generative CML to capture service level requirements and, subsequently, to select the services to honour customer requirements and generate the deployment code appropriate to these services. We present the architectural design of SLO-ML and the associated broker that realises the deployment operations. We rigorously evaluate SLO-ML using a mixed methods approach. First, we exploit an experimental case study with a group of researchers and developers using a real-world cloud application. We also assess overheads through an exhaustive set of empirical scalability tests. Through expressing the levels of gained productivity and experienced usability, we highlight SLO-ML's profound potential in enabling user-centric cloud brokers. We also discuss limitations as application requirements grow.
