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 …

Intelligent load balancing techniques in software defined networks: A survey

T Semong, T Maupong, S Anokye, K Kehulakae… - Electronics, 2020 - mdpi.com
In the current technology driven era, the use of devices that connect to the internet has
increased significantly. Consequently, there has been a significant increase in internet …

Deep reinforcement learning for cooperative content caching in vehicular edge computing and networks

G Qiao, S Leng, S Maharjan, Y Zhang… - IEEE Internet of Things …, 2019 - ieeexplore.ieee.org
In this article, we propose a cooperative edge caching scheme, a new paradigm to jointly
optimize the content placement and content delivery in the vehicular edge computing and …

Ai-based mobile edge computing for iot: Applications, challenges, and future scope

A Singh, SC Satapathy, A Roy, A Gutub - Arabian Journal for Science and …, 2022 - Springer
New technology is needed to meet the latency and bandwidth issues present in cloud
computing architecture specially to support the currency of 5G networks. Accordingly, mobile …

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 …

Deep-reinforcement-learning-based QoS-aware secure routing for SDN-IoT

X Guo, H Lin, Z Li, M Peng - IEEE Internet of things journal, 2019 - ieeexplore.ieee.org
Recently, with the proliferation of communication devices, Internet of Things (IoT) has
become an emerging technology which facilitates massive devices to be enabled with …

Deep reinforcement learning for communication flow control in wireless mesh networks

Q Liu, L Cheng, AL Jia, C Liu - IEEE Network, 2021 - ieeexplore.ieee.org
Wireless mesh network (WMN) is one of the most promising technologies for Internet of
Things (IoT) applications because of its self-adaptive and self-organization nature. To meet …

Machine learning for next‐generation intelligent transportation systems: A survey

T Yuan, W da Rocha Neto… - Transactions on …, 2022 - Wiley Online Library
Intelligent transportation systems, or ITS for short, includes a variety of services and
applications such as road traffic management, traveler information systems, public transit …

Deep reinforcement learning-based routing on software-defined networks

G Kim, Y Kim, H Lim - IEEE Access, 2022 - ieeexplore.ieee.org
With an exponential increase in network traffic demands requiring quality of services, the
need for routing optimization has become more prominent. Recently, the advent of software …

A survey and comparative evaluation of actor‐critic methods in process control

D Dutta, SR Upreti - The Canadian Journal of Chemical …, 2022 - Wiley Online Library
Actor‐critic (AC) methods have emerged as an important class of reinforcement learning
(RL) paradigm that enables model‐free control by acting on a process and learning from the …