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时间敏感网络时隙感知循环排队转发流量整形机制

蔡岳平 任志文

蔡岳平, 任志文. 时间敏感网络时隙感知循环排队转发流量整形机制[J]. 电子与信息学报. doi: 10.11999/JEIT220530
引用本文: 蔡岳平, 任志文. 时间敏感网络时隙感知循环排队转发流量整形机制[J]. 电子与信息学报. doi: 10.11999/JEIT220530
CAI Yueping, REN Zhiwen. Time Slot-Aware Cyclic Queuing and Forwarding Based on Traffic Shaping Mechanism in Time-Sensitive Networking[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT220530
Citation: CAI Yueping, REN Zhiwen. Time Slot-Aware Cyclic Queuing and Forwarding Based on Traffic Shaping Mechanism in Time-Sensitive Networking[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT220530

时间敏感网络时隙感知循环排队转发流量整形机制

doi: 10.11999/JEIT220530
基金项目: 国家重点研发计划(2020YFB1710900)
详细信息
    作者简介:

    蔡岳平:男,副教授,研究方向为泛在确定性网络

    任志文:男,硕士生,研究方向为时间敏感网络

    通讯作者:

    蔡岳平 caiyueping@cqu.edu.cn

  • 中图分类号: TN915

Time Slot-Aware Cyclic Queuing and Forwarding Based on Traffic Shaping Mechanism in Time-Sensitive Networking

Funds: The National Key Research and Development Program of China (2020YFB1710900)
  • 摘要: 时间敏感网络是智能工厂内网的核心技术之一。智能工厂内存在多种需求各异的业务流。为保证关键业务流的性能,同时提升网络带宽利用率,该文提出一种时隙感知循环排队转发流量整形机制(TSA-CQF)。TSA-CQF通过将低优先级流量插入CQF奇偶队列中剩余可用时隙中传输提高带宽利用率。TSA-CQF机制包括低优先级流量的时隙感知插入和全局流量规划两个部分。低优先级流量的时隙感知插入是在CQF队列出队时,通过感知奇偶队列剩余时隙的大小,将低优先级流量插入到奇偶队列的剩余时隙进行传输。将全局流量规划建模为多条件约束目标优化问题,通过模拟退火算法求解,完成全局流量的调度,提高可调度流数目,进一步提高资源利用率。仿真结果表明,在混合流量条件下TSA-CQF比传统CQF机制平均提高了带宽利用率11.29%。与传统的CQF相比,TSA-CQF在牺牲一定调度策略生成时间的前提下,能明显提高带宽利用率并且降低最坏端到端时延。
  • 图  1  周期流量在某一端队列大量聚集

    图  2  固定时隙导致的带宽浪费

    图  3  TSA-CQF机制交换机模型

    图  4  固定时隙中可供调度的时隙

    图  5  TSA-CQF方案步骤

    图  6  SA-TSA算法流程

    图  7  仿真拓扑图

    图  8  不同网络负载下时敏流量端到端最坏时延

    图  9  不同流量占比对时敏流量最坏时延的影响

    图  10  不同网络负载下带宽利用率

    图  11  网络负载与流调度结果生成时间的关系

    表  1  约束参数说明

    参数描述
    $S$流的集合
    ${S_{[a,b]}}$流从发出节点a到接收节点b
    ${T_{{s_i}}}$最坏端到端时延
    ${L_{{s_i}}}$数据大小(以字节为单位)
    ${C_{{s_i}}}$流的周期
    ${f_{[a,b]}}$流中的帧从节点a到节点b
    ${f_{[a,b]}}{\text{ }} \cdot {\text{ }}\phi $流从节点ab的帧映射到时钟上的偏移量
    ${f_{[a,b]}}{\text{ }} \cdot {\text{ }}T$流从节点ab的帧映射到时钟上的周期
    ${f_{[a,b]}}{\text{ }} \cdot {\text{ }}L$流从节点ab的帧映射到时钟上传输大小
    ${F_{[{\text{a}},b]}}$流${S_{[a,b]}}$和帧${f_{[a,b]}}$的集合
    下载: 导出CSV
    算法1 SA-TSA 算法
     输入G //网络拓扑
        F //流资源设置
        Q //队列资源设置
        Ri //流约束设置,i∈[1,5]
     输出Foffset //流偏移量
     Fcur←{G,F,R}_init; //通过输入初始化当前解
     for (i=0;i<N;i++) //循环每一个流
      for (j=1;j<=5;j++) //循环每一个约束条件
       if Fi contain Rj //判断是否满足约束条件
        Qcur←Qfresh; //满足则更新可用资源数
         Continue;
       else
      while Fi contain Rj //不满足则进行偏移循环
        Fcur←F do offset;
      end while;
       end if
      end for
     end for
     while numcur <numitera AND Fi contain Ri //迭代
      Fnew←SA random() compute; //扰动产生新解
       if Fnew better than Fcur //对最优解进行更新
         Fcur =Fnew ;
       end if
       numcur++; //当前迭代次数加1
     end while
     return Fcur; //完成迭代,输出最优解
    下载: 导出CSV

    表  2  仿真参数

    参数分布取值
    链路带宽(Mbit/s)1 000
    网络负载[0.1,1]
    帧数量5×104
    帧大小(Byte)均匀分布[64,500]
    帧到达过程泊松分布500
    奇偶时隙大小(µs)250
    队列长度(MByte)1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-27
  • 修回日期:  2022-10-25
  • 网络出版日期:  2022-11-18

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