Combining graph neural network with deep reinforcement learning for resource allocation in computing force networks
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.
Computing force networkRouting optimizationDeep learningGraph neural networkResource allocation
韩雪莹、谢明熹、禹可、黄小红、杜宗鹏、姚惠娟
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北京邮电大学计算机学院(国家示范性软件学院),中国 北京市,100876
北京邮电大学人工智能学院,中国 北京市,100876
中国移动研究院基础网络技术研究所,中国 北京市,100032
算力网络 路由优化 深度学习 图神经网络 资源分配
Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center
NETL
NSTL
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