通信学报 ›› 2020, Vol. 41 ›› Issue (11): 124-131.doi: 10.11959/j.issn.1000-436x.2020203

• 学术论文 • 上一篇    下一篇

面向大规模时间敏感网络的分组调度机制

邱雪松1,黄徐川1,李文萃2,李温静3,郭少勇1   

  1. 1 北京邮电大学网络与交换技术国家重点实验室,北京 100876
    2 国网河南省电力公司信息通信公司,河南 郑州 450052
    3 国网信息通信产业集团有限公司,北京 102211
  • 修回日期:2020-09-04 出版日期:2020-11-25 发布日期:2020-12-19
  • 作者简介:邱雪松(1973- ),男,江西上饶人,博士,北京邮电大学教授、博士生导师,主要研究方向为网络与业务管理、物联网与区块链|黄徐川(1997- ),男,安徽合肥人,北京邮电大学硕士生,主要研究方向为时间敏感网络|李文萃(1984- ),女,河南许昌人,博士,国网河南电力公司高级工程师,主要研究方向为电力通信网络传输及安全|李温静(1984- ),女,山西太谷人,博士,国网信息通信产业集团有限公司高级工程师,主要研究方向为电力系统边缘计算、电力自动化及终端技术|郭少勇(1985- ),男,河北邢台人,博士,北京邮电大学副教授,主要研究方向为物联网与区块链
  • 基金资助:
    国家电网有限公司总部科技基金资助项目(5700-202024176A-0-0-00)

Group-scheduling mechanism for large-scale time-sensitive network

Xuesong QIU1,Xuchuan HUANG1,Wencui LI2,Wenjing LI3,Shaoyong GUO1   

  1. 1 State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China
    2 Information &Telecommunication Company of State Grid Henan Electric Power Company,Zhengzhou 450052,China
    3 State Grid Information and Telecommunication Group Company Limited,Beijing 102211,China
  • Revised:2020-09-04 Online:2020-11-25 Published:2020-12-19
  • Supported by:
    State Grid Corporation of China Science and Technology Project(5700-202024176A-0-0-00)

摘要:

针对大规模时间敏感网络中时间触发流量的确定性时延问题,提出了一种分组调度机制。所提机制通过拓扑修剪策略和基于谱聚类的流分组策略,避免了由于网络拓扑规模和流量规模剧增对调度响应速度的影响,提升了调度计算效率。仿真实验结果表明,所提机制在大规模调度的场景下,可以在较短时间内进行模型的求解,并且保证一定的调度成功率。

关键词: 时间敏感网络, 时间触发流量, 拓扑修剪, 谱聚类

Abstract:

In order to achieve the deterministic delay of time-triggered traffic in a large-scale time-sensitive network,a kind of group-scheduling mechanism was proposed.By designing a topology pruning strategy and a flow grouping strategy based on spectral clustering,the impact of the rapid increase in network topology scale and traffic scale on the speed of scheduling response was avoided,and the scheduling calculation efficiency was improved.The results of experiment show that the mechanism can solve the model in a relatively short time for large-scale scheduling problem and achieve a relatively high scheduling success rate.

Key words: time-sensitive network, time-triggered traffic, topology pruning, spectral clustering

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