摘要
突发流量在网络中非常普遍,会严重损害用户体验。突发流量往往能在短时间(如毫秒级别)内充满链路,导致网络拥塞和频繁分组丢失,端到端时延增加。传统路由算法要么是流量无关(如OSPF(open shortest path first,开放式最短路径优先))的,无法对实时流量的变化做出调整;要么是集中式控制的(如线性规划),面临求解时延过大而无法有效应对突发流量的问题。提出了一种新的智能路由算法解决突发流量的问题。一方面,提出的算法能利用机器学习强大的建模能力,通过对网络历史数据的挖掘来学习“隐式”的路由决策依据。另一方面,提出的算法能借助机器学习的快速推理能力降低决策时延,提高系统对突发流量的响应速度。实验结果表明,在真实流量数据集下,相比较其他路由算法,提出的智能路由算法能降低13%~70%的瓶颈链路利用率。
Traffic bursts are common in networks,which have a significant impact on quality of user experience.In the case of traffic bursts,huge volumes of packets can overwhelm the physical links in a short time duration(i.e.,milliseconds),resulting in congestion and frequent packet loss.However,traditional routing schemes are either traffic oblivious such as OSPF,which can’t adapt to real-time traffic changes,or centralized control such as linear programming,which can’t efficiently react to traffic bursts due to slow computation.To address this problem in a practical and efficient approach,a novel intelligent routing algorithm based on machine learning(ML)was proposed.On the one hand,the proposed algorithm can leverage the promising modelling ability of machine learning to learn the implicit clue of routing decision.On the other hand,the proposed algorithm enjoys the ultralow processing latency benefited from the fast inference of ML,thus speeding up the reaction to traffic bursts.Experiments on two open-source datasets demonstrate that the proposed scheme can reduce utilization of bottleneck link by 13%~70%,compared with the baselines.
作者
桂飞
程阳
李丹
洪思虹
GUI Fei;CHENG Yang;LI Dan;HONG Sihong(Tsinghua University,Beijing 100084,China)
出处
《电信科学》
2020年第10期12-20,共9页
Telecommunications Science
基金
国家重点研发计划项目(No.2018YFB1800500)
国家自然科学基金资助项目(No.61772305)
广东省重点研发计划项目(No.2018B010113001)。
关键词
互联网路由算法
突发流量
机器学习
深度强化学习
internet routing algorithm
traffic burst
machine learning
deep reinforcement learning
