A Model-Driven Approach to Automate the Deployment and Management of Cloud Services
A Model-Driven Approach to Automate the Deployment and Management of Cloud Services
复制标题
自动化云服务部署和管理的模型驱动方法
DOI：
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发表时间：
2018
期刊：
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通讯作者：
T. Kuroda
中科院分区：
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作者：
Anirban Bhattacharjee;Yogesh D. Barve;A. Gokhale;T. Kuroda
尽管有许多配置工具可用于部署和管理复合云服务，以克服繁琐且容易出错的手动工作，但用户通常需要通过低级脚本来指定结构即代码（IAC）解决方案。IAC需要领域知识来跨异构云平台提供服务，并导致陡峭的学习曲线。为了应对这些挑战，我们提出了一个技术和平台无关的自助服务框架，称为CloudCAMP。它集成了特定于领域的建模，因此云平台和应用程序架构所施加的规范和依赖关系可以在直观的更高抽象级别上指定，而无需领域专业知识。CloudCAMP使用转换生成范式并通过利用可扩展和可重用的知识库将部分规范转换为可部署的结构即代码（IAC）。自动生成的IAC可以由现有工具处理，以自动提供服务组件。我们通过对比研究使用CloudCAMP节省的手动和脚本工作量来定量验证我们的方法。
Although many provisioning tools are available for deployment and management of composite cloud services to overcome the manual efforts that are tedious and error-prone, users are often required to specify Infrastructure-as-Code (IAC) solutions via low-level scripting. IAC demands domain knowledge for provisioning the services across heterogeneous cloud platforms and incurs a steep learning curve. To address these challenges, we present a technology-and platform-agnostic self-service framework called CloudCAMP. It incorporates domain-specific modeling so that the specifications and dependencies imposed by the cloud platform and application architecture can be specified at an intuitive, higher level of abstraction without the need for domain expertise. CloudCAMP transforms the partial specifications into deployable Infrastructure-as-Code (IAC) using the Transformational-Generative paradigm and by leveraging an extensible and reusable knowledge base. The auto-generated IAC can be handled by existing tools to provision the services components automatically. We validate our approach quantitatively by showing a comparative study of savings in manual and scripting efforts versus using CloudCAMP.