Leveraging deep reinforcement learning for traffic engineering: A survey
After decades of unprecedented development, modern networks have evolved far beyond
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
ScaleDRL: A scalable deep reinforcement learning approach for traffic engineering in SDN with pinning control
As modern communication networks become more complicated and dynamic, designing a
good Traffic Engineering (TE) policy becomes difficult due to the complexity of solving the …
good Traffic Engineering (TE) policy becomes difficult due to the complexity of solving the …
Is machine learning ready for traffic engineering optimization?
G Bernárdez, J Suárez-Varela, A López… - 2021 IEEE 29th …, 2021 - ieeexplore.ieee.org
Traffic Engineering (TE) is a basic building block of the Internet. In this paper, we analyze
whether modern Machine Learning (ML) methods are ready to be used for TE optimization …
whether modern Machine Learning (ML) methods are ready to be used for TE optimization …
Deep reinforcement learning for traffic signal control: A review
Traffic congestion is a complex, vexing, and growing issue day by day in most urban areas
worldwide. The integration of the newly emerging deep learning approach and the …
worldwide. The integration of the newly emerging deep learning approach and the …
On deep reinforcement learning for traffic engineering in SD-WAN
S Troia, F Sapienza, L Varé… - IEEE Journal on Selected …, 2020 - ieeexplore.ieee.org
The demand for reliable and efficient Wide Area Networks (WANs) from business customers
is continuously increasing. Companies and enterprises use WANs to exchange critical data …
is continuously increasing. Companies and enterprises use WANs to exchange critical data …
Experience-driven networking: A deep reinforcement learning based approach
Modern communication networks have become very complicated and highly dynamic, which
makes them hard to model, predict and control. In this paper, we develop a novel experience …
makes them hard to model, predict and control. In this paper, we develop a novel experience …
Applications of deep reinforcement learning in communications and networking: A survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
State-of-the-art deep learning: Evolving machine intelligence toward tomorrow's intelligent network traffic control systems
Currently, the network traffic control systems are mainly composed of the Internet core and
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …
Application of deep reinforcement learning in traffic signal control: An overview and impact of open traffic data
Persistent congestions which are varying in strength and duration in the dense traffic
networks are the most prominent obstacle towards sustainable mobility. Those types of …
networks are the most prominent obstacle towards sustainable mobility. Those types of …
A deep reinforcement learning perspective on internet congestion control
We present and investigate a novel and timely application domain for deep reinforcement
learning (RL): Internet congestion control. Congestion control is the core networking task of …
learning (RL): Internet congestion control. Congestion control is the core networking task of …