  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.
