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On-line Access: 2024-06-04

Received: 2023-01-05

Revision Accepted: 2024-06-04

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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.5 P.701-712


Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks

Author(s):  Xueying HAN, Mingxi XIE, Ke YU, Xiaohong HUANG, Zongpeng DU, Huijuan YAO

Affiliation(s):  School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China; more

Corresponding email(s):   hanxueying@bupt.edu.cn, yuke@bupt.edu.cn

Key Words:  Computing force network, Routing optimization, Deep learning, Graph neural network, Resource allocation

Xueying HAN, Mingxi XIE, Ke YU, Xiaohong HUANG, Zongpeng DU, Huijuan YAO. Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(5): 701-712.

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publisher="Zhejiang University Press & Springer",

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%T Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks
%A Xueying HAN
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T1 - Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks
A1 - Xueying HAN
A1 - Mingxi XIE
A1 - Ke YU
A1 - Xiaohong HUANG
A1 - Zongpeng DU
A1 - Huijuan YAO
J0 - Frontiers of Information Technology & Electronic Engineering
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2300009

Fueled by the explosive growth of ultra-low-latency and real-time applications with specific computing and network performance requirements, the computing force network (CFN) has become a hot research subject. The primary CFN challenge is to leverage network resources and computing resources. Although recent advances in deep reinforcement learning (DRL) have brought significant improvement in network optimization, these methods still suffer from topology changes and fail to generalize for those topologies not seen in training. This paper proposes a graph neural network (GNN) based DRL framework to accommodate network traffic and computing resources jointly and efficiently. By taking advantage of the generalization capability in GNN, the proposed method can operate over variable topologies and obtain higher performance than the other DRL methods.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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