Reinforcement Learning-based Multi-domain Network Slice Provisioning

被引：0

作者：

Wu, Zhouxiang ^{[1
]}

Ishigaki, Genya ^{[2
]}

Gour, Riti ^{[3
]}

Li, Congzhou ^{[1
]}

Mi, Feng ^{[1
]}

Talluri, Subhash ^{[4
]}

Jue, Jason P. ^{[1
]}

机构：

[1] Univ Texas Dallas, Dept Comp Sci, Richardson, TX 75080 USA

[2] San Jose State Univ, Dept Comp Sci, San Jose, CA 95192 USA

[3] Cent Connecticut State Univ, Dept Comp Elect & Graph Technol, New Britain, CT 06050 USA

[4] Amazon Web Serv, Seattle, WA 98108 USA

来源：

ICC 2023-IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS | 2023年

基金：

美国国家科学基金会;

关键词：

Network Slice; Resource Allocation; Machine Learning; Reinforcement Learning; Graph Neural Network;

D O I：

10.1109/ICC45041.2023.10278745

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

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.

引用

页码：1899 / 1904

页数：6

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