深度学习在IP网络优化中的应用  

Application of deep learning in IP network optimization

作　　者：曾汉 徐晓青 钱刘熠辉 武娟[1] ZENG Han;XU Xiaoqing;QIAN Liuyihui;WU Juan(China Telecom Research Institute,Guangzhou 510000,China)

机构地区：[1]中国电信股份有限公司研究院,广州510000

出　　处：《信息通信技术与政策》2023年第7期89-96,共8页Information and Communications Technology and Policy

摘　　要：随着新型业务涌现和IP网络技术的不断演进,云网融合步入新阶段,展现出数字化、智能化和服务化的发展特征。其中智能化需要结合相关的人工智能技术,而深度学习和深度强化学习是常用的人工智能算法。图神经网络等技术的发展,也使得深度学习和深度强化学习分别在图信息表示和最优化问题处理方面的能力得到本质提升。IP网络可以用图结构抽象化表示,相关的预测和优化问题可以用深度学习和深度强化学习算法处理和求解。因此阐述了深度学习和深度强化学习在流量预测、网络规划和流量工程3个场景下的相关算法与应用,分析了在实践过程中可能面临的问题与挑战。With the emergence of new services and the continuous evolution of IP network technology,cloud-network convergence has entered a new stage,showing the development characteristics of digitization,intelligence and servitization.Intelligence needs to be combined with relevant artificial intelligence technologies.Deep learning and deep reinforcement learning are commonly used artificial intelligence algorithms.With the development of graph neural network and other technologies,the ability of deep learning to represent graph information and the ability of deep reinforcement learning to deal with optimization problems have been improved.IP networks can be represented abstractly by using graph structures,and related prediction and optimization problems can be processed and solved by using deep learning and deep reinforcement learning algorithms.Therefore,this paper describes the related algorithms and applications of deep learning and deep reinforcement learning in three scenarios including traffic prediction,network planning and traffic engineering,and analyzes the possible problems and challenges that may occur in practice.

关 键 词：深度学习 深度强化学习 流量预测 网络规划 流量工程 云网融合 

分 类 号：TN915.5[电子电信—通信与信息系统]