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

• Published in: Frontiers of information technology & electronic engineering Vol. 25; no. 5; pp. 701 - 712
• Main Authors: Han, Xueying, Xie, Mingxi, Yu, Ke, Huang, Xiaohong, Du, Zongpeng, Yao, Huijuan
• Format: Journal Article
• Language: English
• Published: Hangzhou Zhejiang University Press 01.05.2024; Springer Nature B.V
• Subjects: Bandwidths; Cloud computing; Communications Engineering; Communications networks; Communications traffic; Computation; Computer Hardware; Computer Science; Computer Systems Organization and Communication Networks; Deep learning; Edge computing; Electrical Engineering; Electronics and Microelectronics; Graph neural networks; Instrumentation; Internet service providers; Machine learning; Network latency; Network management systems; Network topologies; Networks; Neural networks; Optimization; Power; Research Article; Resource allocation; Deep learning; 图神经网络; 资源分配; Computing force network; TP393; Graph neural network; 路由优化; Resource allocation; 深度学习; Routing optimization; 算力网络

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. Althou...