Deploying a cloud configuration in a real cloud platform is mostly cost- and time- consuming, as large number of cloud resources have to be rented for the time needed to run the configuration. Thereafter, cloud simulation tools are used as a cheap alternative to test cloud configuration. However, most of the existing cloud simulation tools require extensive technical skills and do not support simulation of any kind of cloud resources. In this context, using a model-driven approach can be helpful as it allows developers to efficiently describe their needs at a high level of abstraction. To do, we propose, in this article, a model-driven engineering approach based on the Open Cloud Computing Interface(OCCI) standard metamodel and CloudSim toolkit. We firstly extend OCCI metamodel for the supporting simulation of any kind of cloud resources. Afterward, to illustrate the extensibility of our approach, we enrich the proposed metamodel by new simulation capabilities. As proof of concept, we study the elasticity and pricing strategies of Amazon Web Services (AWS). This article benefits from OCCIware Studio to design an OCCI simulation extension and to provide a simulation designer for designing cloud configurations to be simulated. We detail the approach process from defining an OCCI simulation extension until the generation and the simulation of the OCCI cloud configurations. Finally, we validate the proposed approach by providing a realistic experimentation to study its usability, the resources coverage rate and the cost. The results are compared with the ones computed from AWS.

中文翻译：

---

弹性OCCI云资源的模型驱动模拟

在实际的云平台上部署云配置通常既费钱又费时，因为在运行配置所需的时间内必须租用大量云资源。此后，云模拟工具被用作测试云配置的廉价替代品。但是，大多数现有的云模拟工具需要广泛的技术技能，并且不支持对任何类型的云资源进行模拟。在这种情况下，使用模型驱动的方法可能会有所帮助，因为它使开发人员可以在较高的抽象水平上有效地描述其需求。为此，我们在本文中提出了一种基于开放式云计算接口（OCCI）标准元模型和CloudSim工具包的模型驱动的工程方法。我们首先扩展OCCI元模型，以支持任何类型的云资源的仿真。然后，为了说明我们方法的可扩展性，我们通过新的仿真功能丰富了提出的元模型。作为概念验证，我们研究了Amazon Web Services（AWS）的弹性和定价策略。本文受益于OCCIware Studio设计OCCI仿真扩展，并提供仿真设计器来设计要仿真的云配置。我们详细介绍了从定义OCCI仿真扩展到生成和仿真OCCI云配置的方法过程。最后，我们通过提供实际实验来研究其可用性，资源覆盖率和成本来验证所提出的方法。将结果与从AWS计算的结果进行比较。