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基于LSTM的TTE网络速率约束流量预测

史亚菲 李峭 熊华钢

史亚菲, 李峭, 熊华钢等 . 基于LSTM的TTE网络速率约束流量预测[J]. 北京航空航天大学学报, 2020, 46(4): 822-829. doi: 10.13700/j.bh.1001-5965.2019.0320
引用本文: 史亚菲, 李峭, 熊华钢等 . 基于LSTM的TTE网络速率约束流量预测[J]. 北京航空航天大学学报, 2020, 46(4): 822-829. doi: 10.13700/j.bh.1001-5965.2019.0320
SHI Yafei, LI Qiao, XIONG Huaganget al. Rate-constrained traffic prediction of TTE network based on LSTM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(4): 822-829. doi: 10.13700/j.bh.1001-5965.2019.0320(in Chinese)
Citation: SHI Yafei, LI Qiao, XIONG Huaganget al. Rate-constrained traffic prediction of TTE network based on LSTM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(4): 822-829. doi: 10.13700/j.bh.1001-5965.2019.0320(in Chinese)

基于LSTM的TTE网络速率约束流量预测

doi: 10.13700/j.bh.1001-5965.2019.0320
基金项目: 

国防科技基金 0101070

中央高校基本科研业务费专项资金 YWF-14-DZXY-018

载人航天预先研究项目 060301

详细信息
    作者简介:

    史亚菲  女, 硕士研究生。主要研究方向:实时网络、航空电子系统综合化互连

    李峭  男, 博士, 讲师, 硕士生导师。主要研究方向:航空电子网络、分布式实时系统

    熊华钢  男, 博士, 教授, 博士生导师。主要研究方向:航空电子综合、机载网络

    通讯作者:

    李峭, E-mail: avionics@buaa.edu.cn

  • 中图分类号: V247;TP393

Rate-constrained traffic prediction of TTE network based on LSTM

Funds: 

National Defense Science and Technology Fund 0101070

the Fundamental Research Funds for the Central Universities YWF-14-DZXY-018

Manned Space Pre-Research Project 060301

More Information
  • 摘要:

    时间触发以太网(TTE)中的速率约束(RC)流量为事件触发流量,在RC流量动态调度的应用场景下,若能预测未来短时间内数条RC流量到达交换节点的序列,使交换节点提前进行调度决策,以减小RC流量时延,提高网络吞吐量。对RC流量到达序列预测问题进行了研究,建立了RC流量的到达序列模型,提出了基于长短期记忆网络(LSTM)算法的RC流量预测算法。利用OMNET++工具进行TTE网络仿真,得到多组混合关键性配置下RC流量的传输数据;以此作为输入样本对预测算法进行训练和测试。实验结果显示,LSTM算法在RC流量预测问题的准确率达到了70%以上。通过对比实验说明所提算法适用于RC流量预测场景。

     

  • 图 1  TTE网络TT、RC流量的传输过程

    Figure 1.  Transmission process of TT and RC traffic in TTE network

    图 2  TTE网络流量传输示例

    Figure 2.  Examples of TTE network traffic transmission

    图 3  RC流量预测排序效果

    Figure 3.  Sorting effect of RC traffic sorting

    图 4  RNN模型

    Figure 4.  RNN model

    图 5  LSTM算法模型

    Figure 5.  LSTM algorithm model

    图 6  LSTM算法详解

    Figure 6.  Details of LSTM algorithm

    图 7  LSTM预测模型数据集构造图

    Figure 7.  Dataset construction map of LSTM prediction model

    图 8  网络拓扑结构示意图

    Figure 8.  Schematic diagram of network topology

    图 9  算法预测值与真实值对比

    Figure 9.  Comparison of predicted value and real value of algorithms

    表  1  TTE网络流量配置

    Table  1.   TTE network traffic configuration

    消息类型 源节点 目的节点 数据帧长/Byte 周期/ms
    TT1 ES1 ES12 528 64
    TT2 ES11 ES21 215 12
    TT3 ES5 ES11 1505 16
    TT4 ES3 ES2 1273 84
    TT5 ES21 ES21 659 40
    RC1 ES1 ES21 688 2
    RC2 ES4 ES12 808 16
    RC3 ES2 ES21 433 4
    RC4 ES3 ES11 433 16
    RC5 ES5 ES21 1518 4
    RC6 ES1 ES4 1022 8
    RC7 ES3 ES21 208 32
    RC8 ES11 ES21 574 2
    RC9 ES3 ES21 828 4
    下载: 导出CSV

    表  2  RC流量传输数据

    Table  2.   Transmission data of RC traffic

    到达时间/ms 流量编号 包帧长/Byte
    3.600852 RC1 688
    4.78901 RC3 433
    5.600852 RC1 688
    7.600852 RC1 688
    8.78901 RC3 433
    9.600852 RC1 688
    10.391926 RC6 1022
    11.600852 RC1 688
    12.78901 RC3 433
    13.600852 RC1 688
    15.600852 RC1 688
    16.78901 RC3 433
    19.4391926 RC6 1022
    203.553353 RC3 433
    207.553353 RC3 433
    210.391926 RC6 1022
    211.553353 RC3 433
    下载: 导出CSV

    表  3  算法预测结果比较

    Table  3.   Comparison of algorithm prediction results

    算法模型 *5%准确率/% *8%准确率/% 均方误差
    LSTM 75.3 93.2 4.5
    RNN 42.8 47.2 40.4
    DNN 33.9 40.8 53
    注:*5%准确率指预测值误差小于RC流量约束最长帧的5%;*8%准确率指预测值误差小于RC流量约束最长帧的8%。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-20
  • 录用日期:  2019-07-05
  • 刊出日期:  2020-04-20

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