RouteNet: Leveraging graph neural networks for network modeling and optimization in SDN
Network modeling is a key enabler to achieve efficient network operation in future self-
driving Software-Defined Networks. However, we still lack functional network models able to …
driving Software-Defined Networks. However, we still lack functional network models able to …
Unveiling the potential of graph neural networks for network modeling and optimization in SDN
Network modeling is a critical component for building self-driving Software-Defined
Networks, particularly to find optimal routing schemes that meet the goals set by …
Networks, particularly to find optimal routing schemes that meet the goals set by …
Graph neural networks for communication networks: Context, use cases and opportunities
Graph neural networks (GNN) have shown outstanding applications in fields where data is
essentially represented as graphs (eg, chemistry, biology, and recommendation systems). In …
essentially represented as graphs (eg, chemistry, biology, and recommendation systems). In …
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 …
[HTML][HTML] Building a digital twin for network optimization using graph neural networks
Network modeling is a critical component of Quality of Service (QoS) optimization. Current
networks implement Service Level Agreements (SLA) by careful configuration of both routing …
networks implement Service Level Agreements (SLA) by careful configuration of both routing …
RouteNet-Fermi: Network modeling with graph neural networks
M Ferriol-Galmés, J Paillisse… - … ACM transactions on …, 2023 - ieeexplore.ieee.org
Network models are an essential block of modern networks. For example, they are widely
used in network planning and optimization. However, as networks increase in scale and …
used in network planning and optimization. However, as networks increase in scale and …
An intelligent route computation approach based on real-time deep learning strategy for software defined communication systems
Software Defined Networking (SDN) is regarded as the next generation paradigm as it
simplifies the structure of the data plane and improves the resource utilization. However, in …
simplifies the structure of the data plane and improves the resource utilization. However, in …
ML-based pre-deployment SDN performance prediction with neural network boosting regression
Software defined networking (SDN) has been proposed as an effective approach to improve
network management efficiency and increase network intelligence in various networks …
network management efficiency and increase network intelligence in various networks …
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 …
Enabling scalable routing in software-defined networks with deep reinforcement learning on critical nodes
P Sun, Z Guo, J Li, Y Xu, J Lan… - IEEE/ACM Transactions …, 2021 - ieeexplore.ieee.org
Traditional routing schemes usually use fixed models for routing policies and thus are not
good at handling complicated and dynamic traffic, leading to performance degradation (eg …
good at handling complicated and dynamic traffic, leading to performance degradation (eg …