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A model-driven approach for deployment descriptor design in network function virtualization
International Journal of Network Management ( IF 1.5 ) Pub Date : 2021-04-27 , DOI: 10.1002/nem.2165
Wassim Sellil Atoui 1, 2 , Nour Assy 3 , Walid Gaaloul 1 , Imen Grida Ben Yahia 2
Affiliation  

Service providers in network function virtualization usually design manually or with static automation the deployment descriptors for virtual network functions. The descriptors are semi-structured files that contain information about the resource requirements and the operational behavior of virtual network functions. Designing the descriptors manually and without formal strategies is certainly a cumbersome and error-prone task for service providers. In this work, we propose a model-driven approach that assists service providers in designing the deployment descriptors. This approach uses a configurable model iteratively to give service providers insights on which best configuration to choose. Concretely, we propose (1) to use a configurable deployment descriptor model, (2) a learning approach based on machine learning to automatically construct the configurable model, and (3) an approach that learns configuration guidelines from a catalog of deployment descriptors to assist service providers with the selection of the configuration to use. The configurable deployment descriptor model captures the relation and also the variability between the virtualized network function (VNF) elements from different deployment descriptors. We propose a learning approach to build the configurable deployment descriptor model by finding and federating similar VNF elements from different deployment descriptors. With our machine learning approach, we construct automatically the configurable model from a set of deployment descriptors. We use afterward the configurable model to learn configuration guidelines from the deployment descriptors and recommend them for service providers. The results of our experiments highlight the effectiveness of our approach to learning configurable deployment descriptor models.

中文翻译:

网络功能虚拟化中部署描述符设计的模型驱动方法

网络功能虚拟化中的服务提供商通常手动或静态自动化设计虚拟网络功能的部署描述符。描述符是半结构化文件,包含有关资源需求和虚拟网络功能的操作行为的信息。在没有正式策略的情况下手动设计描述符对于服务提供商来说无疑是一项繁琐且容易出错的任务。在这项工作中,我们提出了一种模型驱动的方法,可以帮助服务提供商设计部署描述符。这种方法迭代地使用可配置模型,让服务提供商了解选择哪种最佳配置。具体来说,我们建议(1)使用可配置的部署描述符模型,(2) 一种基于机器学习自动构建可配置模型的学习方法,以及 (3) 一种从部署描述符目录中学习配置指南以帮助服务提供商选择要使用的配置的方法。可配置部署描述符模型捕获来自不同部署描述符的虚拟化网络功能 (VNF) 元素之间的关系以及可变性。我们提出了一种学习方法,通过从不同的部署描述符中查找和联合相似的 VNF 元素来构建可配置的部署描述符模型。通过我们的机器学习方法,我们从一组部署描述符中自动构建可配置模型。我们之后使用可配置模型从部署描述符中学习配置指南,并向服务提供商推荐它们。我们的实验结果突出了我们学习可配置部署描述符模型的方法的有效性。
更新日期:2021-04-27
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