计算机工程与应用2025，Vol.61Issue(24)：1-28,28.DOI:10.3778/j.issn.1002-8331.2412-0248
基于强化学习的软件定义网络流量工程研究综述
Survey on Traffic Engineering in Software-Defined Networking Based on Reinforcement Learning
摘要
Abstract
Software-defined networking(SDN),with its global and centralized management architecture,has brought rev-olutionary changes to the management of complex and dynamic networks,and has also created favorable conditions for network traffic engineering.Concurrently,reinforcement learning has garnered significant attention due to its pronounced advantages in decision optimization.The integration of reinforcement learning with the unique architecture of SDN and its application to SDN traffic engineering holds substantial practical significance.Firstly,from both theoretical and practical perspectives,based on the trajectory of technological development,the paper reviews the advancements in reinforcement learning,deep reinforcement learning,and multi-agent deep reinforcement learning in SDN traffic engineering.Addition-ally,it conducts a thorough synthesis and analysis of existing research outcomes across various dimensions,including methodological categorization,network scenarios,reinforcement learning algorithms,and traffic engineering objectives,providing a multidimensional perspective on the integration of reinforcement learning with SDN traffic engineering.Sub-sequently,it further summarizes the research progress of reinforcement learning combined with other technologies,dem-onstrating its potential to enhance the performance of traffic engineering.Ultimately,based on a summary of the current research progress,the paper analyzes the challenges faced and proposes future research directions,providing some refer-ence for deepening exploration in this domain.关键词
强化学习/软件定义网络/流量工程/路由算法Key words
reinforcement learning/software-defined networking/traffic engineering/routing algorithm分类
信息技术与安全科学引用本文复制引用
LIU Yanfei,WANG Chengjin,LI Chao..基于强化学习的软件定义网络流量工程研究综述[J].计算机工程与应用,2025,61(24):1-28,28.基金项目
国家自然科学基金青年基金(62301596) （62301596）
国家自然科学基金面上项目(U23B2064). （U23B2064）
