A survey of machine learning techniques applied to software defined networking (SDN): Research issues and challenges

J Xie, FR Yu, T Huang, R Xie, J Liu… - … Surveys & Tutorials, 2018 - ieeexplore.ieee.org
In recent years, with the rapid development of current Internet and mobile communication
technologies, the infrastructure, devices and resources in networking systems are becoming …

Towards artificial intelligence enabled 6G: State of the art, challenges, and opportunities

S Zhang, D Zhu - Computer Networks, 2020 - Elsevier
Abstract 6G is expected to support the unprecedented Internet of everything scenarios with
extremely diverse and challenging requirements. To fulfill such diverse requirements …

[HTML][HTML] A review of the use of artificial intelligence methods in infrastructure systems

L McMillan, L Varga - Engineering Applications of Artificial Intelligence, 2022 - Elsevier
The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the
growth of digitalisation and has the potential to enable the 'system of systems' approach …

Intelligent routing based on reinforcement learning for software-defined networking

DM Casas-Velasco, OMC Rendon… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Traditional routing protocols employ limited information to make routing decisions, which
can lead to a slow adaptation to traffic variability, as well as restricted support to the Quality …

A survey of networking applications applying the software defined networking concept based on machine learning

Y Zhao, Y Li, X Zhang, G Geng, W Zhang, Y Sun - IEEE access, 2019 - ieeexplore.ieee.org
The main task of future networks is to build, as much as possible, intelligent networking
architectures for intellectualization, activation, and customization. Software-defined …

A survey on machine learning techniques for routing optimization in SDN

R Amin, E Rojas, A Aqdus, S Ramzan… - IEEE …, 2021 - ieeexplore.ieee.org
In conventional networks, there was a tight bond between the control plane and the data
plane. The introduction of Software-Defined Networking (SDN) separated these planes, and …

DROM: Optimizing the routing in software-defined networks with deep reinforcement learning

C Yu, J Lan, Z Guo, Y Hu - IEEE Access, 2018 - ieeexplore.ieee.org
This paper proposes DROM, a deep reinforcement learning mechanism for Software-
Defined Networks (SDN) to achieve a universal and customizable routing optimization …

A topical review on machine learning, software defined networking, internet of things applications: Research limitations and challenges

Imran, Z Ghaffar, A Alshahrani, M Fayaz, AM Alghamdi… - Electronics, 2021 - mdpi.com
In recent years, rapid development has been made to the Internet of Things communication
technologies, infrastructure, and physical resources management. These developments and …

Artificial intelligence enabled software‐defined networking: a comprehensive overview

M Latah, L Toker - IET networks, 2019 - Wiley Online Library
Software‐defined networking (SDN) represents a promising networking architecture that
combines central management and network programmability. SDN separates the control …

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 …