GRL-PS: Graph embedding-based DRL approach for adaptive path selection
W Wei, L Fu, H Gu, Y Zhang, T Zou… - … on Network and …, 2023 - ieeexplore.ieee.org
Forwarding path selection for data traffic is one of the most fundamental operations in
computer networks, whose performance drastically impacts both transmission efficiency and …
computer networks, whose performance drastically impacts both transmission efficiency and …
G-Routing: Graph Neural Networks-Based Flexible Online Routing
H Wei, Y Zhao, K Xu - IEEE Network, 2023 - ieeexplore.ieee.org
Deep reinforcement learning (DRL) has been widely used to find optimal routing schemes to
meet various demands of users. However, the optimization goal of DRL is typically static …
meet various demands of users. However, the optimization goal of DRL is typically static …
Packet routing with graph attention multi-agent reinforcement learning
X Mai, Q Fu, Y Chen - 2021 IEEE Global Communications …, 2021 - ieeexplore.ieee.org
Packet routing is a fundamental problem in communication networks that decides how the
packets are directed from their source nodes to their destination nodes through some …
packets are directed from their source nodes to their destination nodes through some …
[HTML][HTML] An approach to combine the power of deep reinforcement learning with a graph neural network for routing optimization
B Chen, D Zhu, Y Wang, P Zhang - Electronics, 2022 - mdpi.com
Routing optimization has long been a problem in the networking field. With the rapid
development of user applications, network traffic is continuously increasing in dynamicity …
development of user applications, network traffic is continuously increasing in dynamicity …
Toward greater intelligence in route planning: A graph-aware deep learning approach
Software-defined networking decouples the control plane and data plane, which grants
more computing power for routing computations. Traditional routing methods suffer from the …
more computing power for routing computations. Traditional routing methods suffer from the …
A Graph reinforcement learning based SDN routing path selection for optimizing long-term revenue
J Xu, Y Wang, B Zhang, J Ma - Future Generation Computer Systems, 2024 - Elsevier
Abstract Software-Defined Network (SDN) paradigm decouples control plane from data
plane and provides a logically-centralized control to whole underlying network, which …
plane and provides a logically-centralized control to whole underlying network, which …
Generative adversarial network-based transfer reinforcement learning for routing with prior knowledge
With the incremental deployment of software defined networking, the routing algorithms
have gained more power on observability and controllability. Deep reinforcement learning …
have gained more power on observability and controllability. Deep reinforcement learning …
Multi-agent reinforcement learning for adaptive routing: A hybrid method using eligibility traces
Packet routing in communication networks is a natural problem for sequential decision-
making. Previously, many heuristic methods are proposed based on domain knowledge …
making. Previously, many heuristic methods are proposed based on domain knowledge …
[HTML][HTML] ENERO: Efficient real-time WAN routing optimization with Deep Reinforcement Learning
Abstract Wide Area Networks (WAN) are a key infrastructure in today's society. During the
last years, WANs have seen a considerable increase in network's traffic and network …
last years, WANs have seen a considerable increase in network's traffic and network …
Dealing with changes: Resilient routing via graph neural networks and multi-agent deep reinforcement learning
SS Bhavanasi, L Pappone… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The computer networking community has been steadily increasing investigations into
machine learning to help solve tasks such as routing, traffic prediction, and resource …
machine learning to help solve tasks such as routing, traffic prediction, and resource …