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

Xueying HAN; Mingxi XIE; Ke YU; Xiaohong HUANG; Zongpeng DU; Huijuan YAO

doi:10.1631/FITEE.2300009

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

Special Feature on Coordination of Networking and Computing: Architecture, Theory, and Practice | Updated：2024-06-03

- Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks
- 图神经网络与深度强化学习结合的算力网络资源分配方法
- “In the realm of computing force networks, a new research breakthrough has been achieved. Experts have developed a graph neural network-based deep reinforcement learning framework that optimizes network resources and computing resources. This innovation offers a solution to the challenges posed by the dynamic nature of network topologies, significantly advancing the field of network optimization.”
- Frontiers of Information Technology & Electronic Engineering Vol. 25, Issue 5, Pages: 701-712(2024)
- Affiliations：
  
  1.School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
  2.School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
  3.Department of Infrastructure Network Technology Research, China Mobile Research Institute, Beijing 100032, China
- Author bio：
  
  E-mail: hanxueying@bupt.edu.cn
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2300009
  CLC： TP393
- Received：05 January 2023，
  
  Accepted：24 April 2023，
  
  Published：0 May 2024
- Accepted：
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Xueying HAN, Mingxi XIE, Ke YU, et al. 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.
DOI：

Xueying HAN, Mingxi XIE, Ke YU, et al. 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. DOI： 10.1631/FITEE.2300009.

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

DOI：10.1631/FITEE.2300009