Deep reinforcement learning for resource management on network slicing: A survey

JA Hurtado Sánchez, K Casilimas… - Sensors, 2022 - mdpi.com
Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving
5G and 6G networks. A 5G/6G network can comprise various network slices from unique or …

In-network machine learning using programmable network devices: A survey

C Zheng, X Hong, D Ding, S Vargaftik… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
Machine learning is widely used to solve networking challenges, ranging from traffic
classification and anomaly detection to network configuration. However, machine learning …

Leveraging deep reinforcement learning for traffic engineering: A survey

Y Xiao, 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 …

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 …

Routing optimization with deep reinforcement learning in knowledge defined networking

Q He, Y Wang, X Wang, W Xu, F Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Traditional routing algorithms cannot dynamically change network environments due to the
limited information for routing decisions. Meanwhile, they are prone to performance …

A comprehensive survey on knowledge-defined networking

PADSN Wijesekara, S Gunawardena - Telecom, 2023 - mdpi.com
Traditional networking is hardware-based, having the control plane coupled with the data
plane. Software-Defined Networking (SDN), which has a logically centralized control plane …

DRSIR: A deep reinforcement learning approach for routing in software-defined networking

DM Casas-Velasco, OMC Rendon… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traditional routing protocols employ limited information to make routing decisions, which
leads to slow adaptation to traffic variability and restricted support to the quality of service …

ALBRL: Automatic Load‐Balancing Architecture Based on Reinforcement Learning in Software‐Defined Networking

J Chen, Y Wang, J Ou, C Fan, X Lu… - Wireless …, 2022 - Wiley Online Library
Due to the rapid development of network communication technology and the significant
increase in network terminal equipment, the application of new network architecture …

MCEAACO-QSRP: A novel QoS-secure routing protocol for industrial Internet of Things

C Li, Y Liu, J Xiao, J Zhou - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
With the widespread application of the Industrial Internet of Things (IIoT), the requirements
for sensing equipment to collect data and information continue to increase, and industrial …

Intelligent routing method based on Dueling DQN reinforcement learning and network traffic state prediction in SDN

L Huang, M Ye, X Xue, Y Wang, H Qiu, X Deng - Wireless Networks, 2024 - Springer
The traditional routing method makes use of limited information on the network links to make
routing decisions, which makes it difficult to adapt to the dynamic and complex network and …