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.

翻译：全球互联网交通史无前例的增长,加上大量时空波动,在某种程度上造成可预测的潮汐交通条件,正在推动从被动到主动和最终向适应性光学网络的演变,在这些网络中,由交通驱动的服务提供可以解决网络供不应求的问题,更好地适应交通变化,同时将服务质量保持在所需的水平上,这种做法将减少网络资源供不应求,从而减少网络总成本;这项调查全面审查了光学层以机器为基础的技术在提供交通驱动服务方面的先进情况;首先介绍了光学网络提供服务的发展情况,随后概述了用于提供交通驱动服务的ML技术;详细介绍了提供ML辅助服务的办法,包括在主动性和适应性网络中预测性和规范性服务提供框架;还介绍了关于所有技术的局限性、研究挑战和潜在机会的讨论。