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基于灾难预测多区域故障的虚拟光网络生存性映射

刘焕淋 杜理想 陈勇 王展鹏

刘焕淋, 杜理想, 陈勇, 王展鹏. 基于灾难预测多区域故障的虚拟光网络生存性映射[J]. 电子与信息学报, 2020, 42(7): 1710-1717. doi: 10.11999/JEIT190561
引用本文: 刘焕淋, 杜理想, 陈勇, 王展鹏. 基于灾难预测多区域故障的虚拟光网络生存性映射[J]. 电子与信息学报, 2020, 42(7): 1710-1717. doi: 10.11999/JEIT190561
Huanlin LIU, Lixiang DU, Yong CHEN, Zhanpeng WANG. Disaster Prediction-based Survivable Virtual Optical Network Mapping for Multi-Area Faults[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1710-1717. doi: 10.11999/JEIT190561
Citation: Huanlin LIU, Lixiang DU, Yong CHEN, Zhanpeng WANG. Disaster Prediction-based Survivable Virtual Optical Network Mapping for Multi-Area Faults[J]. Journal of Electronics & Information Technology, 2020, 42(7): 1710-1717. doi: 10.11999/JEIT190561

基于灾难预测多区域故障的虚拟光网络生存性映射

doi: 10.11999/JEIT190561
基金项目: 国家自然科学基金(51977021);重庆市自然科学基金面上项目(2019jcyj-msxmX0613)
详细信息
    作者简介:

    刘焕淋:女,1970年生,教授,研究方向为光通信技术与未来网络

    杜理想:男,1995年生,硕士,研究方向为光网络生存性路由算法

    陈勇:男,1963年生,教授,研究方向为光通信技术、传感检测与自动化技术

    王展鹏:男,1996年生,硕士,研究方向为光网络生存性调度算法

    通讯作者:

    刘焕淋 liuhl2@sina.com

  • 中图分类号: TN929.11

Disaster Prediction-based Survivable Virtual Optical Network Mapping for Multi-Area Faults

Funds: The National Natural Science Foundation of China (51977021), The Natural Science Foundation Project of Chongqing Science and Technology Commission (2019 jcyj-msxmX0613)
  • 摘要:

    生存性虚拟光网络映射是提高光网络应对灾难故障的重要技术保障措施。为解决灾难性多区域故障导致弹性光网络的带宽容量损失问题,该文提出基于灾难预测故障模型的蚁群优化虚拟光网络映射 (DFM-ACO-VNM)算法。在该算法中,设计基于光节点资源和相邻链路的全局潜在故障概率的光节点排序映射准则,并设计启发式信息公式实现多区域故障下最小带宽容量损失的虚拟节点和虚拟链路协同映射。仿真结果表明,该文所提算法在多区域故障时能降低带宽容量损失,减少带宽阻塞率和提高频谱利用率。

  • 图  1  虚拟网络映射到灾难弹性光网络示意图

    图  2  仿真网络多区域灾难故障拓扑

    图  3  不同负载下带宽阻塞率的对比

    图  4  不同负载下带宽容量损失的对比

    图  5  不同负载下频谱利用率的对比

    表  1  DFM-ACO-VNM算法

     输入:输入底层网络${{{G}}_{\rm{s}}}({{{N}}_{\rm{s}}},{{{{\rm E}}}_{\rm{s}}})$和灾难事件F,虚拟网络请求${{{G}}_{\rm{v}}}({{{N}}_{\rm{v}}},{{{E}}_{\rm{v}}})$。
     输出:Antbest,即虚拟网络的虚拟节点映射,虚拟链路映射和频谱资源分配结果。
     (1)  初始化信息素浓度矩阵τ[n][m],启发式信息矩阵η[n][m],转移概率矩阵P[n][m],初始化${A_{{\rm{best}}}} = 1000000$, nNv集合的虚拟节点
        数,mNs集合的光节点数,设置蚁群算法最大迭代次数Gmax,迭代变量j=0; Aj为虚拟网络蚁群映射第j轮结果;
     (2)  根据灾难集合F,计算EONs区域A灾难概率${p_A}\left( {{f_i}} \right)\;$,根据式(7)计算各光纤链路es的灾难评估$M({e_{\rm{s}}})$值;
     (3)  根据式(5),对虚拟网络中的虚拟节点降序排列在集合${R_{{n_{\rm{v}}}}}$中;
     (4)  For(j =0, j+1, j <Gmax)
     (5)   For 从排序第1个虚拟节点到第n个虚拟节点
     (6)    执行EBCL-VNM映射算法(表2),构造虚拟网络映射解Aj
     (7)   End for
     (8)    If ${{\rm{E}}_{{\rm{BCL}}}}({A_{{\rm{best}}}})$ > ${{{E}}_{{\rm{BCL}}}}({A_j})$
     (9)     令${{{E}}_{{\rm{BCL}}}}({A_{{\rm{best}}}})$ = ${{{E}}_{{\rm{BCL}}}}({A_j})$
     (10)    End if
     (11)    根据式(10),更新信息素浓度矩阵
     (12)    if converge
     (13)     Break;
     (14)    end if
     (15)  end for
     (16)  Return ${{\rm{E}}_{{\rm{BCL}}}}({A_{{\rm{best}}}})$ and ${A_{{\rm{best}}}}$
    下载: 导出CSV

    表  2  EBCL-VNM 算法

     (1)  初始化虚拟网络请求的虚拟节点、链路映射结果集合和资源分配结合,即A0 = Φ
     (2)  根据式(6),排序EONs中光节点在集合${R_{{n_{\rm{s}}}}}$中;
     (3)  选择顺序列表${R_{{n_{\rm{v}}}}}$中的第1个虚拟节点$n_{\rm{v}}^0$;
     (4) 选择顺序列表${R_{{n_{\rm{s}}}}}$中的第1个光节点$n_{\rm{s}}^0$;
     (5)  将满足资源约束的$n_{\rm{v}}^0$映射到$n_{\rm{s}}^0$,记录已映射节点信息,并从集合${R_{{n_{\rm{v}}}}}$中删除$n_{\rm{v}}^0$;
     (6)  For 依次映射集合${R_{{n_{\rm{v}}}}}$的剩余虚拟节点
     (7)  当前拟映射虚拟节点$n_v^i$加入已映射虚拟节点和虚拟链路集合时,根据式(8)结果确定需要新映射的虚拟链路;
     (8)  找出满足虚拟节点资源约束条件的候选光节点集合$n_{\rm{s}}^{i{\rm{ - C}}}$;
     (9)   For 对所有候选光节点集合$n_{\rm{s}}^{i{\rm{ - C}}}$依次执行
     (10)    运行多商品流算法映射各虚拟链路的K条候选光路路由和资源光路带宽分配;
     (11)    根据式(8)计算启发式信息矩阵η[$n_{\rm{v}}^i$][$n_{\rm{s}}^j$];
     (12)    根据式(9)计算转移概率矩阵P[$n_{\rm{v}}^i$][$n_{\rm{s}}^j$];
     (13)    将虚拟节点i按式(9)计算值,概率地选择映射到光节点j
     (14)   End for
     (15)   更新虚拟节点和虚拟链路映射集合;
     (16)  End for
     (17)  Return 虚拟节点映射、虚拟链路映射和资源分配结果。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-07-25
  • 修回日期:  2020-02-19
  • 网络出版日期:  2020-03-12
  • 刊出日期:  2020-07-23

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