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基于图卷积神经网络的软件定义电力通信网络路由控制策略

向敏 饶华阳 张进进 陈梦鑫

向敏, 饶华阳, 张进进, 陈梦鑫. 基于图卷积神经网络的软件定义电力通信网络路由控制策略[J]. 电子与信息学报, 2021, 43(2): 388-395. doi: 10.11999/JEIT190971
引用本文: 向敏, 饶华阳, 张进进, 陈梦鑫. 基于图卷积神经网络的软件定义电力通信网络路由控制策略[J]. 电子与信息学报, 2021, 43(2): 388-395. doi: 10.11999/JEIT190971
Min XIANG, Huayang RAO, Jinjin ZHANG, Mengxin CHEN. Software-defined Power Communication Network Routing Control Strategy Based on Graph Convolution Network[J]. Journal of Electronics and Information Technology, 2021, 43(2): 388-395. doi: 10.11999/JEIT190971
Citation: Min XIANG, Huayang RAO, Jinjin ZHANG, Mengxin CHEN. Software-defined Power Communication Network Routing Control Strategy Based on Graph Convolution Network[J]. Journal of Electronics and Information Technology, 2021, 43(2): 388-395. doi: 10.11999/JEIT190971

基于图卷积神经网络的软件定义电力通信网络路由控制策略

doi: 10.11999/JEIT190971
基金项目: 国家电网公司总部科技项目(52010118000Q)
详细信息
    作者简介:

    向敏:男,1974年生,教授,研究方向为智能电网、工业物联网及自动化控制

    饶华阳:女,1995年生,硕士生,研究方向为软件定义电力通信网络的流量协同控制

    张进进:女,1994年生,硕士生,研究方向为软件定义电力通信网络的业务弹性QoS保障

    陈梦鑫:男,1996年生,硕士生,研究方向为软件定义电力通信网络的集中控制架构

    通讯作者:

    饶华阳 s170301016@stu.cqupt.edu.cn

  • 中图分类号: TN915.853

Software-defined Power Communication Network Routing Control Strategy Based on Graph Convolution Network

Funds: The Project of Science and Technology of State Grid Corporation of China (52010118000Q)
  • 摘要:

    传输时延和数据包丢失率是电力通信业务可靠传输重点关注的问题,该文提出一种面向软件定义电力通信网络的最小路径选择度路由控制策略。结合电力通信网络软件定义网络(SDN)集中控制架构的特点,利用图卷积神经网络构建的链路带宽占用率预测模型(LBOP-GCN)分析下一时刻路径带宽占用率。通过三角模算子(TMO)融合路径的传输时延、当前时刻的路径带宽占用率和下一时刻的路径带宽占用率,计算出从源节点到目的节点间不同传输路径的选择度(Q),然后将Q值最小的路径作为SDN控制器下发的流表项。实验结果表明,该文所提出的路由控制策略能有效减小业务传输时延和数据包丢失率。

  • 图  1  电力通信网络SDN架构

    图  2  LBOP-GCN模型总体架构

    图  3  t时刻的路径带宽占用率

    图  4  t+T时刻的路径带宽占用率

    图  5  基于SDN的最小路径选择度路由控制策略示意图

    图  6  LBOP-GCN模型预测值与真实值对比

    图  7  MPSRCS路由策略与SPRS, HCARS策略性能对比

    表  1  OpenFlow交换机端口和流表状态参数

    端口参数符号说明流表参数符号说明
    $p_{a,q}^1(t)$rx_packets接收的数据包数$f_a^1(t)$length交换机流表容量
    $p_{a,q}^2(t)$tx_packets转发的数据包数$f_a^2(t)$priority流表项匹配次序
    $p_{a,q}^3(t)$rx_bytes接收的字节数$f_a^3(t)$packet_count根据流表转发的数据包数
    $p_{a,q}^4(t)$tx_bytes转发的字节数$f_a^4(t)$byte_count根据流表转发的字节数
    $p_{a,q}^5(t)$rx_dropped接收时丢弃的数据包数$f_a^5(t)$duration_sec数据流持续时间
    $p_{a,q}^6(t)$tx_dropped转发时丢弃的数据包数$f_a^6(t)$duration_nsec数据流额外生存时间
    $p_{a,q}^7(t)$tx_errors转发时错误的数据包数$f_a^7(t)$idle_timeout流表项从交换机移除的相对时间
    $p_{a,q}^8(t)$rx_frame_err接收时错误帧的数$f_a^8(t)$hard_timeout流表项从交换机移除的绝对时间
    $p_{a,q}^9(t)$rx_over_eer接收时溢出的数据包数
    下载: 导出CSV

    表  2  链路带宽占用率等级

    ${\mu _j}(t)$${\mu _j}(t)$等级链路拥塞状态${s_j}(t)$
    0~0.6无拥塞1
    0.6~0.7正常负荷2
    0.7~0.8可能拥塞3
    0.8~0.9一般拥塞4
    超过0.9严重拥塞5
    下载: 导出CSV
  • 兰巨龙, 于倡和, 胡宇翔, 等. 基于深度增强学习的软件定义网络路由优化机制[J]. 电子与信息学报, 2019, 41(11): 2669–2674. doi: 10.11999/JEIT180870

    LAN Julong, YU Changhe, HU Yuxiang, et al. SDN routing optimization mechanism based on deep reinforcement learning[J]. Journal of Electronics &Information Technology, 2019, 41(11): 2669–2674. doi: 10.11999/JEIT180870
    史久根, 谢熠君, 孙立, 等. 软件定义网络中面向时延和负载的多控制器放置策略[J]. 电子与信息学报, 2019, 41(8): 1869–1876. doi: 10.11999/JEIT181053

    SHI Jiugen, XIE Yijun, SUN Li, et al. Multi-controller placement strategy based on latency and load in software defined network[J]. Journal of Electronics &Information Technology, 2019, 41(8): 1869–1876. doi: 10.11999/JEIT181053
    张红旗, 黄睿, 常德显. 一种基于匹配博弈的服务链协同映射方法[J]. 电子与信息学报, 2019, 41(2): 385–393. doi: 10.11999/JEIT180385

    ZHANG Hongqi, HUANG Rui, and CHANG Dexian. A collaborative mapping method for service chain based on matching game[J]. Journal of Electronics &Information Technology, 2019, 41(2): 385–393. doi: 10.11999/JEIT180385
    于天放, 芮兰兰, 邱雪松. 基于软件定义网络的服务器集群负载均衡技术研究[J]. 电子与信息学报, 2018, 40(12): 3028–3035. doi: 10.11999/JEIT180207

    YU Tianfang, RUI Lanlan, and QIU Xuesong. Research on SDN-based load balancing technology of server cluster[J]. Journal of Electronics &Information Technology, 2018, 40(12): 3028–3035. doi: 10.11999/JEIT180207
    AL-RUBAYE S, KADHUM E, NI Qiang, et al. Industrial internet of things driven by SDN platform for smart grid resiliency[J]. IEEE Internet of Things Journal, 2019, 6(1): 267–277. doi: 10.1109/JIOT.2017.2734903
    AUJLA G S, GARG S, BATRA S, et al. DROpS: A demand response optimization scheme in SDN-enabled smart energy ecosystem[J]. Information Sciences, 2019, 476: 453–473. doi: 10.1016/j.ins.2018.09.047
    胡宇翔, 李子勇, 胡宗魁, 等. 基于流量工程的软件定义网络控制资源优化机制[J]. 电子与信息学报, 2020, 42(3): 661–668. doi: 10.11999/JEIT190276

    HU Yuxiang, LI Ziyong, HU Zongkui, et al. Control resource optimization mechanism of SDN based on traffic engineering[J]. Journal of Electronics &Information Technology, 2020, 42(3): 661–668. doi: 10.11999/JEIT190276
    熊余, 杨娅娅, 张振振, 等. 软件定义时分波分复用无源光网络中基于带宽预测的资源分配策略[J]. 电子与信息学报, 2019, 41(8): 1885–1892. doi: 10.11999/JEIT180837

    XIONG Yu, YANG Yaya, ZHANG Zhenzhen, et al. Resource allocation based on bandwidth prediction in software-defined time and wavelength division multiplexed passive optical network[J]. Journal of Electronics &Information Technology, 2019, 41(8): 1885–1892. doi: 10.11999/JEIT180837
    伊鹏, 刘洪, 胡宇翔. 一种可扩展的软件定义数据中心网络流调度策略[J]. 电子与信息学报, 2017, 39(4): 825–831. doi: 10.11999/JEIT160623

    YI Peng, LIU Hong, and HU Yuxiang. A scalable traffic scheduling policy for software defined data center network[J]. Journal of Electronics &Information Technology, 2017, 39(4): 825–831. doi: 10.11999/JEIT160623
    王继业, 刘川, 吴军民, 等. 软件定义电力广域网通信业务资源公平分配技术研究[J]. 电网技术, 2015, 39(5): 1425–1431. doi: 10.13335/j.1000-3673.pst.2015.05.038

    WANG Jiye, LIU Chuan, WU Junmin, et al. Research of software defined service resource equitable allocation technology of power WAN[J]. Power System Technology, 2015, 39(5): 1425–1431. doi: 10.13335/j.1000-3673.pst.2015.05.038
    韩宇奇, 何宜倩, 楼凤丹, 等. 基于SDN的动态优化路由策略在信息物理融合电力系统连锁故障中的研究与应用[J]. 电网技术, 2018, 42(8): 2620–2629. doi: 10.13335/j.1000-3673.pst.2017.1796

    HAN Yuqi, HE Yiqian, LOU Fengdan, et al. Analysis and application of SDN based dynamic optimal route strategy for cyber layer in cascading failures of cyber-physical power system[J]. Power System Technology, 2018, 42(8): 2620–2629. doi: 10.13335/j.1000-3673.pst.2017.1796
    TOMOVIC S and RADUSINOVIC I. RO-RO: Routing optimality-reconfiguration overhead balance in software-defined ISP networks[J]. IEEE Journal on Selected Areas in Communications, 2019, 37(5): 997–1011. doi: 10.1109/JSAC.2019.2906762
    MANESSI F, ROZZA A, and MANZO M. Dynamic graph convolutional networks[J]. Pattern Recognition, 2020, 97: 107000. doi: 10.1016/j.patcog.2019.107000
    ZHANG Zhengchao, LI Meng, LIN Xi, et al. Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies[J]. Transportation Research Part C: Emerging Technologies, 2019, 105: 297–322. doi: 10.1016/j.trc.2019.05.039
    PARISOT S, KTENA S I, FERRANTE E, et al. Disease prediction using graph convolutional networks: Application to autism spectrum disorder and Alzheimer’s disease[J]. Medical Image Analysis, 2018, 48: 117–130. doi: 10.1016/j.media.2018.06.001
    贾惠彬, 薛凯夫, 马静, 等. 广域保护通信多路径路由选择的改进蚁群算法[J]. 电力系统自动化, 2016, 40(22): 22–26. doi: 10.7500/AEPS20160612005

    JIA Huibin, XUE Kaifu, MA Jing, et al. Improved ant colony algorithm for multi-path routing selection in wide-area protection communication system[J]. Automation of Electric Power Systems, 2016, 40(22): 22–26. doi: 10.7500/AEPS20160612005
    SUN Yi, YU Li, ZHANG Jing, et al. Joint MAC-PHY layer resource allocation algorithm based on triangle module operator for multi-service OFDM system[J]. Procedia Environmental Sciences, 2011, 10: 163–169. doi: 10.1016/j.proenv.2011.09.029
    WANG Junfeng, MIAO Yiming, ZHOU Ping, et al. A software defined network routing in wireless multihop network[J]. Journal of Network and Computer Applications, 2017, 85: 76–83. doi: 10.1016/j.jnca.2016.12.007
    AI-KASHOASH H A A, AMER H M, MIHAYLOVA L, et al. Optimization-based hybrid congestion alleviation for 6LoWPAN networks[J]. IEEE Internet of Things Journal, 2017, 4(6): 2070–2081. doi: 10.1109/jiot.2017.2754918
    樊冰, 唐良瑞. 电力通信网脆弱性分析[J]. 中国电机工程学报, 2014, 34(7): 1191–1197. doi: 10.13334/j.0258-8013.pcsee.2014.07.022

    FAN Bing and TANG Liangrui. Vulnerability analysis of power communication network[J]. Proceedings of the CSEE, 2014, 34(7): 1191–1197. doi: 10.13334/j.0258-8013.pcsee.2014.07.022
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出版历程
  • 收稿日期:  2019-12-03
  • 修回日期:  2020-04-24
  • 网络出版日期:  2020-05-31
  • 刊出日期:  2021-02-23

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