Disaster Prediction-based Survivable Virtual Optical Network Mapping for Multi-Area Faults
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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。
Abstract:Survivable virtual optical network mapping is an important technology to improve the optical network response to disaster failures. In order to solve the problem of bandwidth capacity loss caused by multi-area faults resulted from disasters in Elastic Optical Networks (EONs), a multi-area disaster fault model of survivable virtual network based on risk assessment is established, and a Disaster Fault Model based Ant Colony Optimization for Virtual Network Mapping (DFM-ACO-VNM) algorithm is proposed in the paper. An optical node ranking mapping criterion based on node resources and global potential failure probability of adjacent links in EONs is designed. Then, a heuristic information formula is designed to realize cooperative mapping of virtual nodes and virtual links with minimum bandwidth capacity loss under multi-area faults. The simulation results show that the proposed algorithm can decrease the bandwidth capacity loss, reduce the bandwidth blocking probability and improve the spectrum utilization in multi-area faults.
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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${A_{{\rm{best}}}} = 1000000$, n为Nv集合的虚拟节点
数，m为Ns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 ${A_{{\rm{best}}}}$ 
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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。
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