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arXiv 2008.00937cs.NIcs.AIcs.LG
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NFV管理和演示中对高级智能的需求

The Need for Advanced Intelligence in NFV Management and Orchestration

University of Western Ontario(西安大略大学)

Dimitrios Michael Manias, Abdallah Shami

摘要:

随着对连接的持续需求达到历史最高水平,网络服务提供商(NSP)需要优化其网络,以应对不断增长的资本和运营支出,以满足不断增长的连接需求。通过网络功能虚拟化(NFV)提出了对该挑战的解决方案。随着网络复杂性的增加和未来网络的形成,NSP需要将越来越多的运营效率纳入其支持NFV的网络。一种这样的技术是机器学习(ML),其已经被应用于支持NFV的网络中的各种实体,最显著的是在NFV增强器中。虽然传统的机器学习提供了巨大的运营效率,包括实时和大容量的数据处理,但隐私,安全性,可扩展性,可转移性和概念漂移等挑战阻碍了其广泛的实施。通过采用先进的智能技术,如强化学习和联邦学习,NSP可以利用传统ML的优势,同时解决传统上与之相关的主要挑战。这项工作提出了采用这些先进技术的好处,提供了潜在的用例和研究主题列表,并提出了一种自下而上的微功能方法,将这些先进的智能方法应用于NFV管理和演示。

英文摘要:

With the constant demand for connectivity at an all-time high, Network Service Providers (NSPs) are required to optimize their networks to cope with rising capital and operational expenditures required to meet the growing connectivity demand. A solution to this challenge was presented through Network Function Virtualization (NFV). As network complexity increases and futuristic networks take shape, NSPs are required to incorporate an increasing amount of operational efficiency into their NFV-enabled networks. One such technique is Machine Learning (ML), which has been applied to various entities in NFV-enabled networks, most notably in the NFV Orchestrator. While traditional ML provides tremendous operational efficiencies, including real-time and high-volume data processing, challenges such as privacy, security, scalability, transferability, and concept drift hinder its widespread implementation. Through the adoption of Advanced Intelligence techniques such as Reinforcement Learning and Federated Learning, NSPs can leverage the benefits of traditional ML while simultaneously addressing the major challenges traditionally associated with it. This work presents the benefits of adopting these advanced techniques, provides a list of potential use cases and research topics, and proposes a bottom-up micro-functionality approach to applying these methods of Advanced Intelligence to NFV Management and Orchestration.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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