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 …
temporal fluctuations that create, to some extent, predictable tidal traffic conditions, are …
On deep reinforcement learning for static routing and wavelength assignment
N Di Cicco, EF Mercan, O Karandin… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Deep Reinforcement Learning (DRL) is rising as a promising tool for solving optimization
problems in optical networks. Though studies employing DRL for solving static optimization …
problems in optical networks. Though studies employing DRL for solving static optimization …
An overview of ML-based applications for next generation optical networks
Over the past few decades, the demand for the capacity and reliability of optical networks
has continued to grow. In the meantime, optical networks with larger knowledge scales have …
has continued to grow. In the meantime, optical networks with larger knowledge scales have …
Deep reinforcement learning for comprehensive route optimization in elastic optical networks using generative strategies.
The latest advances in Deeper Reinforcement Learning (DRL) have completely changed
how decision-making and automatic control issues are solved. The study community …
how decision-making and automatic control issues are solved. The study community …
Resource allocation in multicore elastic optical networks: a deep reinforcement learning approach
A deep reinforcement learning (DRL) approach is applied, for the first time, to solve the
routing, modulation, spectrum, and core allocation (RMSCA) problem in dynamic multicore …
routing, modulation, spectrum, and core allocation (RMSCA) problem in dynamic multicore …
Deep reinforcement learning-based RMSA policy distillation for elastic optical networks
B Tang, YC Huang, Y Xue, W Zhou - Mathematics, 2022 - mdpi.com
The reinforcement learning-based routing, modulation, and spectrum assignment has been
regarded as an emerging paradigm for resource allocation in the elastic optical networks …
regarded as an emerging paradigm for resource allocation in the elastic optical networks …
Routing and spectrum assignment employing long short-term memory technique for elastic optical networks
L Cheng, Y Qiu - Optical Switching and Networking, 2022 - Elsevier
With the prevalence of some high bandwidth-demanding applications, such as cloud
computing, traditional wavelength-division-multiplexing passive optical networks have …
computing, traditional wavelength-division-multiplexing passive optical networks have …
Multi-agent and cooperative deep reinforcement learning for scalable network automation in multi-domain SD-EONs
The service provisioning in multi-domain software-defined elastic optical networks (SD-
EONs) is an interesting but difficult problem to tackle, because the basic problem of lightpath …
EONs) is an interesting but difficult problem to tackle, because the basic problem of lightpath …
Traffic-based adaptive bandwidth adjustment for flexible OTN connectivity in optical networks
In conventional optical transport networks, the service form is the fixed bandwidth
connectivity, which is not flexible for carrying bursting traffic. To support the time-varying …
connectivity, which is not flexible for carrying bursting traffic. To support the time-varying …
OpticGAI: Generative AI-aided Deep Reinforcement Learning for Optical Networks Optimization
Deep Reinforcement Learning (DRL) is regarded as a promising tool for optical network
optimization. However, the flexibility and efficiency of current DRL-based solutions for optical …
optimization. However, the flexibility and efficiency of current DRL-based solutions for optical …