电讯技术2025，Vol.65Issue(8)：1221-1230,10.DOI:10.20079/j.issn.1001-893x.240520002
基于深度强化学习的工业SDN网络切片资源分配
Industrial SDN Network Slicing Resource Allocation Based on Deep Reinforcement Learning
摘要
Abstract
To address the issue of low network resource utilization caused by the diversity of business requirements and differences in quality of service(QoS)demands in industrial Interhot of Thiugs(IoT),a network slicing resource allocation strategy based on deep reinforcement learning is proposed.This strategy uses deep reinforcement learning to optimize the admission control of network slicing resource allocation.It processes resource requests within a specific time window through an agent and dynamically allocates resources based on the QoS requirements of different network slices and the admission results of the requests.Experimental results show that the proposed strategy improves network revenue,resource utilization,and acceptance rate by 8.33%,9.84%,and 8.57%,respectively,compared with the baseline algorithm.The strategy can improve the efficiency and performance of the entire network while ensuring service quality.关键词
工业物联网(IIOT)/软件定义网络/网络切片/资源分配/准入控制/深度强化学习Key words
industrinal IoT(IIOT)/software-defined networking/network slicing/resource allocation/admission control/deep reinforcement learning分类
信息技术与安全科学引用本文复制引用
张晓莉,雷雨声,刘夏茜,王斌..基于深度强化学习的工业SDN网络切片资源分配[J].电讯技术,2025,65(8):1221-1230,10.基金项目
国家自然科学基金资助项目(U19B2015) （U19B2015）
