浏览全部资源
扫码关注微信
1. 华中科技大学电子信息与通信学院,湖北 武汉 430074
2. 华中科技大学计算机科学与技术学院,湖北 武汉 430074
[ "伍仲丽(1998− ),女,华中科技大学电子信息与通信学院硕士生,主要研究方向为强化学习、路由计算" ]
[ "曹园园(1996− ),女,华中科技大学电子信息与通信学院硕士生,主要研究方向为机器学习、SDN" ]
[ "黄文睿(1999− ),男,华中科技大学电子信息与通信学院硕士生,主要研究方向为图神经网络、网络路由" ]
[ "戴彬(1977− ),男,博士,华中科技大学电子信息与通信学院副教授,主要研究方向为智能路由、边缘计算" ]
[ "莫益军(1976− ),男,博士,华中科技大学计算机科学与技术学院副教授,主要研究方向为智能网络、边缘计算" ]
网络出版日期:2021-11,
纸质出版日期:2021-11-20
移动端阅览
伍仲丽, 曹园园, 黄文睿, 等. 面向确定性网络的按需智能路由技术[J]. 电信科学, 2021,37(11):11-16.
Zhongli WU, Yuanyuan CAO, Wenrui HUANG, et al. On-demand intelligent routing technology for deterministic network[J]. Telecommunications science, 2021, 37(11): 11-16.
伍仲丽, 曹园园, 黄文睿, 等. 面向确定性网络的按需智能路由技术[J]. 电信科学, 2021,37(11):11-16. DOI: 10.11959/j.issn.1000-0801.2021245.
Zhongli WU, Yuanyuan CAO, Wenrui HUANG, et al. On-demand intelligent routing technology for deterministic network[J]. Telecommunications science, 2021, 37(11): 11-16. DOI: 10.11959/j.issn.1000-0801.2021245.
确定性网络需要保证不同应用在时延、丢包率、抖动、吞吐量和可靠性等方面的确定性传输需求。针对应用的差异化、确定性的网络传输需求,提出了一种面向确定性网络的按需智能路由学习框架 OdR,在OdR框架下提出一种基于深度强化学习的按需智能路由算法OdR-TD3,OdR-TD3算法可以根据应用流量的确定性QoS需求生成路由策略,以满足确定性网络应用的需求。通过网络仿真实验评估,在确定性应用的QoS需求达成率上,OdR-TD3算法相较DV算法和SPF算法,具有显著的优势。
Deterministic network needs to ensure the deterministic transmission requirements of different applications in terms of delay
packet loss rate
jitter
throughput
and reliability.In response to the differentiated and deterministic network transmission requirements of applications
an on-demand intelligent routing framework OdR for deterministic network was proposed.Under the OdR framework
an on-demand intelligent routing algorithm named OdR-TD3 based on deep reinforcement learning was proposed
which generates routing strategies based on the deterministic QoS requirements of application traffic
to satisfy the applications’ requirements of deterministic network.The experimental evaluation results show the OdR-TD3 algorithm has a significant advantage over the DV algorithm and the SPF algorithm in terms of the achievement rate of deterministic QoS requirements.
KARAKUS M , DURRESI A . Quality of service (QoS) in software defined networking (SDN):a survey [J ] . Journal of Network and Computer Applications , 2017 ( 80 ): 200 - 218 .
黄韬 , 汪硕 , 黄玉栋 , 等 . 确定性网络研究综述 [J ] . 通信学报 , 2019 , 40 ( 6 ): 160 - 176 .
HUANG T , WANG S , HUANG Y D , et al . Survey of the deterministic network [J ] . Journal on Communications , 2019 , 40 ( 6 ): 160 - 176 .
LI Z M , PENGC , YUG , et al . DetNet:abackbone network for object detection [J ] . 2018.arXiv:1804.06215 .
李季明 , 张宁 . 具有随机性的确定性网络模型 [J ] . 复杂系统与复杂性科学 , 2007 , 4 ( 2 ): 56 - 61 .
LI J M , ZHANG N . Deterministic network model with randomness [J ] . Complex Systems and Complexity Science , 2007 , 4 ( 2 ): 56 - 61 .
LUONG N C , HOANG D T , GONG S M , et al . Applications of deep reinforcement learning in communications and networking:a survey [J ] . IEEE Communications Surveys & Tutorials , 2019 , 21 ( 4 ): 3133 - 3174 .
ROBINSON Y H , JULIE E G , SARAVANAN K , et al . FD-AOMDV:fault-tolerant disjoint ad-hoc on-demand multipath distance vector routing algorithm in mobile ad-hoc networks [J ] . Journal of Ambient Intelligence and Humanized Computing , 2019 , 10 ( 11 ): 4455 - 4472 .
WANG Z , CROWCROFT J . Analysis of shortest-path routing algorithms in a dynamic network environment [J ] . ACM SIGCOMM Computer Communication Review , 1992 , 22 ( 2 ): 63 - 71 .
DAI B , CAO Y Y , WU Z L , et al . Routing optimization meets machine intelligence:a perspective for the future network [J ] . Neurocomputing , 2021 , 459 : 44 - 58 .
XIE J F , YU F R , HUANG T , et al . A survey of machine learning techniques applied to software defined networking (SDN):research issues and challenges [J ] . IEEE Communications Surveys & Tutorials , 2019 , 21 ( 1 ): 393 - 430 .
MCKEOWNN , ANDERSONT , BALAKRISHNANH , et al . OpenFlow [J ] . ACM SIGCOMM Computer Communication Review , 2008 , 38 ( 2 ): 69 - 74 .
SCOTT F , HERKE H , DAVID M . Addressing function approximation error in actor-critic methods [C ] // Proceedings of the 35th International Conference on Machine Learning .[S.l.:s.n. ] , 2018 : 1587 - 1596 .
UHLIG S , QUOITIN B , LEPROPRE J , et al . Providing public intradomain traffic matrices to the research community [J ] . ACM SIGCOMM Computer Communication Review , 2006 , 36 ( 1 ): 83 - 86 .
0
浏览量
442
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构