Fault and performance management in multi-cloud virtual network services using AI: A tutorial and a case study

Fault and performance management in multi-cloud virtual network services using AI: A tutorial and a case study
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使用 AI 进行多云虚拟网络服务中的故障和性能管理:教程和案例研究

DOI:
10.1016/j.comnet.2019.106950
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发表时间:
2019
期刊:
影响因子:
5.6
通讯作者:
Jain, Raj
Jain, Raj
中科院分区:
计算机科学3区
文献类型:
--
作者:
Gupta, Lav;Salman, Tara;Zolanvari, Maede;Erbad, Aiman;Jain, Raj

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运营商发现网络功能虚拟化(NFV)和多云计算是部署其网络服务的有效组合。由此产生的虚拟网络服务(VNS)为它们提供了极大的灵活性和成本优势。然而,赋予这样的服务与传统网络的性能和可用性水平类似,已被证明是一个困难的问题,学者和从业者一样。造成这种复杂性的原因有很多。NFV和基于多云的VNS的故障和性能问题管理的挑战性是一个重要原因。传统物理网络中使用的基于规则的技术在虚拟环境中不能很好地工作。幸运的是,人工智能(AI)的机器和深度学习技术在这种情况下被证明是有效的。本教程的主要目标是了解基于AI的技术如何帮助进行故障检测和定位,以使此类服务更接近传统网络的性能和可用性。为了更好地理解这些概念,还包括了一项基于我们在这一领域工作的案例研究。
Carriers find Network Function Virtualization (NFV) and multi-cloud computing a potent combination for deploying their network services. The resulting virtual network services (VNS) offer great flexibility and cost advantages to them. However, vesting such services with a level of performance and availability akin to traditional networks has proved to be a difficult problem for academics and practitioners alike. There are a number of reasons for this complexity. The challenging nature of management of fault and performance issues of NFV and multi-cloud based VNSs is an important reason. Rule-based techniques that are used in the traditional physical networks do not work well in the virtual environments. Fortunately, machine and deep learning techniques of Artificial Intelligence (AI) are proving to be effective in this scenario. The main objective of this tutorial is to understand how AI-based techniques can help in fault detection and localization to take such services closer to the performance and availability of the traditional networks. A case study, based on our work in this area, has been included for a better understanding of the concepts.
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