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：全球互联网流量的空前增长，加之可预测潮汐式流量条件在大时空尺度波动下的部分可预测性，正推动光网络从反应式向主动式乃至自适应方向演进。在此类网络中，流量驱动的业务供给可在保持服务质量达标的同时，解决网络过度预配置问题并更好地适配流量变化。这种方案将减少网络资源过度配置，从而降低总体网络成本。本综述全面梳理了基于机器学习的光层流量驱动业务供给技术研究现状。首先阐述光网络业务供给的演进历程，继而概述用于流量驱动业务供给的机器学习技术，详细介绍了机器学习辅助的业务供给方法，包括主动式与自适应网络中的预测性与规范性业务供给框架。针对所有列述的技术，还讨论了其局限性、研究挑战及潜在机遇。