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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2300009


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, 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


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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, 1998, -1(-1): .

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pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300009"
}

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%A Zongpeng DU
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Abstract: 
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 both network resources and computing resources jointly. 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 the GNN, the proposed method can operate over variable topologies and obtain high performance superior to the other DRL methods.

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