GraphNET: Graph Neural Networks for routing optimization in Software Defined Networks

In this paper, a graph neural net-based routing algorithm is designed which leverages global information from controller of a software-defined network to predict optimal path with minimum average delay between source and destination nodes in software-defined networks. Graph nets are used because of...

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Bibliographic Details
Published in:Computer communications Vol. 178; pp. 169 - 182
Main Authors: Swaminathan, Avinash, Chaba, Mridul, Sharma, Deepak Kumar, Ghosh, Uttam
Format: Journal Article
Language:English
Published: Elsevier B.V 01.10.2021
Subjects:
Deep Q-learning
Deep Reinforcement Learning
Graph Neural Networks
Q-table
Routing algorithm
Routing optimization
Software-Defined Networking
Q-table
Routing algorithm
Software-Defined Networking
Deep Reinforcement Learning
Routing optimization
Deep Q-learning
Graph Neural Networks
ISSN:0140-3664, 1873-703X
Online Access:Get full text
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Summary:In this paper, a graph neural net-based routing algorithm is designed which leverages global information from controller of a software-defined network to predict optimal path with minimum average delay between source and destination nodes in software-defined networks. Graph nets are used because of their generalization capability which allows the routing algorithm to scale across varying topologies, traffic schemes and changing conditions. A deep reinforcement learning framework is developed to train the Graph Neural Networks using prioritized experience replay from the experiences learnt by the controllers. The algorithm is tested on various small and large topologies in terms of packets successfully routed and average packet delay time. Experiments are performed to check robustness of routing algorithms to changes in network structure and effects of varying hyperparameters. The proposed algorithm shows impressive results when compared to q-routing and shortest path routing algorithm in terms of above experiments and is robust to varying graphical structure of the network.
ISSN:0140-3664
1873-703X
DOI:10.1016/j.comcom.2021.07.025