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arXiv 2209.05080cs.NI
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光网络中业务驱动业务提供的机器学习研究综述

Survey on Machine Learning for Traffic-Driven Service Provisioning in Optical Networks

Tania Panayiotou, Maria Michalopoulou, Georgios Ellinas

摘要:

全球互联网流量的空前增长,加上在某种程度上创造了可预测的潮汐流量条件的大的时空波动,正在推动从被动到主动并最终向自适应光网络的演变。在这些网络中,业务驱动的服务供应可以解决网络过度供应的问题,并更好地适应业务变化,同时保持服务质量在所需的水平。这种方法将减少网络资源过度配置,从而降低总网络成本。本调查提供了一个全面的审查,在光层的机器学习(ML)为基础的技术,用于流量驱动的服务供应的最新技术。首先介绍了光网络中服务提供的演变,然后概述了用于流量驱动服务提供的ML技术。详细介绍了ML辅助的服务提供方法,包括在主动和自适应网络中的预测性和规定性服务提供框架。对于所概述的所有技术,还讨论了它们的局限性,研究挑战和潜在的机会。

英文摘要:

The unprecedented growth of the global Internet traffic, coupled with the large spatio-temporal fluctuations that create, to some extent, predictable tidal traffic conditions, are motivating the evolution from reactive to proactive and eventually towards adaptive optical networks. In these networks, traffic-driven service provisioning can address the problem of network over-provisioning and better adapt to traffic variations, while keeping the quality-of-service at the required levels. Such an approach will reduce network resource over-provisioning and thus reduce the total network cost. This survey provides a comprehensive review of the state of the art on machine learning (ML)-based techniques at the optical layer for traffic-driven service provisioning. The evolution of service provisioning in optical networks is initially presented, followed by an overview of the ML techniques utilized for traffic-driven service provisioning. ML-aided service provisioning approaches are presented in detail, including predictive and prescriptive service provisioning frameworks in proactive and adaptive networks. For all techniques outlined, a discussion on their limitations, research challenges, and potential opportunities is also presented.

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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