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

arXiv: https://arxiv.org/abs/2209.05080 · HTML: https://mlsanity.com/paper/2209.05080/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2022.

## Built on by (most cited first, 3 of 3)

- [Multi-Step Traffic Prediction for Multi-Period Planning in Optical Networks](https://mlsanity.com/paper/2404.08314/) (2024)
- [ConvLSTMTransNet: A Hybrid Deep Learning Approach for Internet Traffic Telemetry](https://mlsanity.com/paper/2409.13179/) (2024)
- [FedNET: Federated Learning for Proactive Traffic Management and Network Capacity Planning](https://mlsanity.com/paper/2511.06797/) (2025)

## Abstract (arXiv)

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.