Deep Reinforcement Learning meets Graph Neural Networks: An optical network routing use case

10/16/2019
by   Paul Almasan, et al.
0

Recent advances in Deep Reinforcement Learning (DRL) have shown a significant improvement in decision-making problems. The networking community has started to investigate how DRL can provide a new breed of solutions to relevant optimization problems, such as routing. However, most of the state-of-the-art DRL-based networking techniques fail to generalize, this means that they can only operate over network topologies seen during training, but not over new topologies. The reason behind this important limitation is that existing DRL networking solutions use standard neural networks (e.g., fully connected), which are unable to learn graph-structured information. In this paper we propose to use Graph Neural Networks (GNN) in combination with DRL. GNN have been recently proposed to model graphs, and our novel DRL+GNN architecture is able to learn, operate and generalize over arbitrary network topologies. To showcase its generalization capabilities, we evaluate it on an Optical Transport Network (OTN) scenario, where the agent needs to allocate traffic demands efficiently. Our results show that our DRL+GNN agent is able to achieve outstanding performance in topologies unseen during training.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/23/2019

Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN

Network modeling is a critical component for building self-driving Softw...
research
09/22/2021

ENERO: Efficient Real-Time Routing Optimization

Wide Area Networks (WAN) are a key infrastructure in today's society. Du...
research
02/01/2022

Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies

The recent growth of emergent network applications (e.g., satellite netw...
research
12/23/2022

Proximal Policy Optimization with Graph Neural Networks for Optimal Power Flow

Optimal Power Flow (OPF) is a very traditional research area within the ...
research
11/10/2022

A Graph Neural Networks based Framework for Topology-Aware Proactive SLA Management in a Latency Critical NFV Application Use-case

Recent advancements in the rollout of 5G and 6G have led to the emergenc...
research
06/06/2022

Efficient entity-based reinforcement learning

Recent deep reinforcement learning (DRL) successes rely on end-to-end le...

Please sign up or login with your details

Forgot password? Click here to reset