Reinforcement Learning-Based Multi-Domain Network Slice Provisioning
Reinforcement Learning-Based Multi-Domain Network Slice Provisioning
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DOI：
10.1109/icc45041.2023.10278745
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Zhouxiang Wu;Genya Ishigaki;Riti Gour;Congzhou Li;Feng Mi;Subhash Talluri;Jason P. Jue
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文献类型：
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作者：
Zhouxiang Wu;Genya Ishigaki;Riti Gour;Congzhou Li;Feng Mi;Subhash Talluri;Jason P. Jue
我们解决了跨多个域建立端到端网络切片的问题，并提出了一个基于强化学习的框架，该框架使多个域能够在端到端网络切片准入和分配上进行协作。目标是使网络运营商的长期收益最大化。我们使用图神经网络（GNN）捕获拓扑特征作为编码器。仿真结果表明，与贪婪算法相比，我们的框架使网络运营商的利润提高了15%。
We address the problem of establishing an end-to-end network slice across multiple domains and propose a Reinforcement Learning-based framework that enables multiple domains to collaborate on end-to-end network slicing admission and allocation. The objective is to maximize the long-term revenue of the network operator. We employ a Graph Neural Network (GNN) to capture the topology features as the encoder. The simulation results show that our framework improves the profit of the network operator by up to 15% compared to a greedy algorithm.