摘要
随着网络应用的蓬勃发展与网络场景的日益多样化,拥塞控制算法的设计面临着前所未有的挑战。人工智能方法凭借强大的自适应性与决策能力,成为学术界和工业界关注的焦点。因此,基于人工智能方法的网络拥塞控制算法应运而生。系统梳理了近年来基于人工智能方法的网络拥塞控制研究进展,从技术途径、应用场景和训练与实验等方面展开分析,并在此基础上展望未来的研究方向。
With the rapid development of network applications and the increasing diversification of network scenarios,the design of congestion control algorithms faces unprecedented challenges.Artificial intelligence(AI)methods,leveraging their robust adaptability and decision-making capabilities,have become a focal point for both academia and industry.Consequently,AI-based network congestion control algorithms have emerged.This paper systematically reviews recent advancements in AI-based network congestion control research,analyzing technical approaches,application scenarios,training,and experimentation.Building on this analysis,future research directions are also explored.
作者
李天云
李韬
温冬
杨惠
张毓涛
罗欣
董德尊
LI Tianyun;LI Tao;WEN Dong;YANG Hui;ZHANG Yutao;LUO Xin;DONG Dezun(College of Computer Science and Technology,National University of Defense Technology,Changsha 410073,China)
出处
《计算机工程与科学》
北大核心
2025年第6期1018-1027,共10页
Computer Engineering & Science
关键词
网络拥塞控制
机器学习
强化学习
深度强化学习
network congestion control
machine learning
reinforcement learning
deep reinforcement learning
