A survey of machine learning techniques applied to software defined networking (SDN): Research issues and challenges
In recent years, with the rapid development of current Internet and mobile communication
technologies, the infrastructure, devices and resources in networking systems are becoming …
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 …
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 …
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 …
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
The main task of future networks is to build, as much as possible, intelligent networking
architectures for intellectualization, activation, and customization. Software-defined …
architectures for intellectualization, activation, and customization. Software-defined …
A survey on machine learning techniques for routing optimization in SDN
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 …
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 …
Defined Networks (SDN) to achieve a universal and customizable routing optimization …
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 …
combines central management and network programmability. SDN separates the control …
A topical review on machine learning, software defined networking, internet of things applications: Research limitations and challenges
In recent years, rapid development has been made to the Internet of Things communication
technologies, infrastructure, and physical resources management. These developments and …
technologies, infrastructure, and physical resources management. These developments and …
QR-SDN: Towards reinforcement learning states, actions, and rewards for direct flow routing in software-defined networks
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 …
packet traffic flows over different network paths. While the centralized control of Software …