Machine learning techniques for optical performance monitoring and modulation format identification: A survey

WS Saif, MA Esmail, AM Ragheb… - … Surveys & Tutorials, 2020 - ieeexplore.ieee.org
The trade-off between more user bandwidth and quality of service requirements introduces
unprecedented challenges to the next generation smart optical networks. In this regard, the …

Leveraging deep reinforcement learning for traffic engineering: A survey

Y **ao, J Liu, J Wu, N Ansari - IEEE Communications Surveys & …, 2021 - ieeexplore.ieee.org
After decades of unprecedented development, modern networks have evolved far beyond
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …

URLLC for 5G and beyond: Requirements, enabling incumbent technologies and network intelligence

R Ali, YB Zikria, AK Bashir, S Garg, HS Kim - IEEE Access, 2021 - ieeexplore.ieee.org
The tactile internet (TI) is believed to be the prospective advancement of the internet of
things (IoT), comprising human-to-machine and machine-to-machine communication. TI …

Survey on machine learning for traffic-driven service provisioning in optical networks

T Panayiotou, M Michalopoulou… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
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 …

QR-SDN: Towards reinforcement learning states, actions, and rewards for direct flow routing in software-defined networks

J Rischke, P Sossalla, H Salah, FHP Fitzek… - IEEE …, 2020 - ieeexplore.ieee.org
Flow routing can achieve fine-grained network performance optimizations by routing distinct
packet traffic flows over different network paths. While the centralized control of Software …

Machine learning for optical fiber communication systems: An introduction and overview

JW Nevin, S Nallaperuma, NA Shevchenko, X Li… - Apl Photonics, 2021 - pubs.aip.org
Optical networks generate a vast amount of diagnostic, control, and performance monitoring
data. When information is extracted from these data, reconfigurable network elements and …

Drl-based deadline-driven advance reservation allocation in eons for cloud–edge computing

R Zhu, G Li, P Wang, M Xu, S Yu - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
The ongoing roll-out of cloud–edge computing and Internet of Things (IoT) has been
simulating the boom of new advance reservation (AR) services, such as bulk-data migration …

GNN-based hierarchical deep reinforcement learning for NFV-oriented online resource orchestration in elastic optical DCIs

B Li, Z Zhu - Journal of Lightwave Technology, 2022 - opg.optica.org
Network function virtualization (NFV) in elastic optical datacenter interconnections (EO-
DCIs) enables flexible and timely deployment of network services. However, as the service …

Flexible technologies to increase optical network capacity

A Lord, SJ Savory, M Tornatore… - Proceedings of the …, 2022 - ieeexplore.ieee.org
Increased global traffic puts tough requirements not just on fiber communications links but
on the entire network. This manifests itself in multiple ways, including how to optimize …

[HTML][HTML] Overview on routing and resource allocation based machine learning in optical networks

Y Zhang, J **n, X Li, S Huang - Optical Fiber Technology, 2020 - Elsevier
For optical networks, routing and resource allocation which considerably determines the
resource efficiency and network capacity is one of the most important works. It has been …