基于DRL的6G多租户网络切片智能资源分配算法

doi:10.13190/j.jbupt.2020-211

北京邮电大学学报 ›› 2020, Vol. 43 ›› Issue (6): 132-139.doi: 10.13190/j.jbupt.2020-211

基于DRL的6G多租户网络切片智能资源分配算法

管婉青¹, 张海君^1,2, 路兆铭^3,4

1. 北京科技大学计算机与通信工程学院, 北京 100083;
2. 北京科技大学人工智能研究院, 北京 100083;
3. 北京邮电大学网络体系构建与融合北京市重点实验室, 北京 100876;
4. 北京邮电大学先进信息网络北京实验室, 北京 100876

收稿日期:2020-10-14 出版日期:2020-12-28 发布日期:2020-11-30
通讯作者: 张海君(1986-),男,教授,E-mail:zhanghaijun@ustb.edu.cn. E-mail:zhanghaijun@ustb.edu.cn
作者简介:管婉青(1995-),女,讲师.
基金资助:
国家重点研发计划项目（2019YFB1803304）；中国传媒大学媒体融合与传播国家重点实验室开放课题（SKLMCC2020KF010）；中央高校基本科研业务费项目（FRF-TP-19-051A1）；北京高校高精尖学科"北京科技大学-人工智能科学与工程"项目

Intelligent Resource Allocation Algorithm for 6G Multi-Tenant Network Slicing Based on Deep Reinforcement Learning

GUAN Wan-qing¹, ZHANG Hai-jun^1,2, LU Zhao-ming^3,4

1. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China;
2. Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China;
3. Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications, Beijing 100876, China;
4. Beijing Laboratory of Advanced Information Networks, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2020-10-14 Online:2020-12-28 Published:2020-11-30

摘要/Abstract

摘要： 未来第6代移动通信系统（6G）网络服务支持虚实结合、实时交互，亟需快速匹配多租户个性化服务需求，对此，提出了一种两层递阶的网络切片智能管理方案，上层部署全局资源管理器，下层部署面向不同租户的本地资源管理器.首先，考虑不同租户多类型切片请求的差异性，基于端到端切片的实时状态描述建立服务质量评估模型.结合服务质量反馈，利用深度强化学习（DRL）算法，优化上层全局资源分配和下层局部资源调整，提升不同域多维资源的使用效益，并使能租户资源定制化.仿真结果表明，所提方案能够在优化资源供应商长期收益的同时，保障服务质量.

关键词: 第6代移动通信系统, 多租户网络切片, 智能管理, 深度强化学习

Abstract: In the future, the sixth generation of mobile communications system (6G) network services merge reality and virtual reality, and support real-time interaction. It is urgent to quickly match the personalized service requirements of multiple tenants, therefore a two-layer hierarchical intelligent management scheme for network slicing is proposed, including the global resource manager at the upper level and the local resource managers for different tenants at the lower level. Firstly, based on the real-time status description of end-to-end slice, a service quality evaluation model is established considering the difference of multi-type slice requests from different tenants. With the service quality feedback, deep reinforcement learning (DRL) algorithm is adopted to optimize the global resource allocation and local resource adjustment. Hence, utilization efficiency of multi-dimensional resources in different domains are improved and tenants are able to customize resource usage. The simulation results show that the proposed scheme can optimize the long-term revenue of resource providers while guaranteeing the service quality.

Key words: the sixth generation of mobile communications system, multi-tenant network slicing, intelligent management, deep reinforcement learning

中图分类号:

TN929.53

管婉青, 张海君, 路兆铭. 基于DRL的6G多租户网络切片智能资源分配算法[J]. 北京邮电大学学报, 2020, 43(6): 132-139.

GUAN Wan-qing, ZHANG Hai-jun, LU Zhao-ming. Intelligent Resource Allocation Algorithm for 6G Multi-Tenant Network Slicing Based on Deep Reinforcement Learning[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(6): 132-139.

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基于DRL的6G多租户网络切片智能资源分配算法

Intelligent Resource Allocation Algorithm for 6G Multi-Tenant Network Slicing Based on Deep Reinforcement Learning

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