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Combining Graph Neural Network with Deep Reinforcement Learning for Resource Allocation in Computing Force Networks
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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): .
@article{title="Combining Graph Neural Network with Deep Reinforcement Learning for Resource Allocation in Computing Force Networks",
author="Xueying HAN, Mingxi XIE, Ke YU, Xiaohong HUANG, Zongpeng DU, Huijuan YAO",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300009"
}
%0 Journal Article
%T Combining Graph Neural Network with Deep Reinforcement Learning for Resource Allocation in Computing Force Networks
%A Xueying HAN
%A Mingxi XIE
%A Ke YU
%A Xiaohong HUANG
%A Zongpeng DU
%A Huijuan YAO
%J Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300009
TY - JOUR
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 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2300009
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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