Service Function Chain Deployment Method for Delay and Reliability Optimization
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摘要: 针对5G网络高可靠性、低时延的服务需求，该文提出一种面向时延与可靠性优化的服务功能链(SFC)部署(DROSD)方法。在不预留冗余资源的情况下，首先通过功能互斥约束来确定SFC中相邻虚拟网络功能(VNF)是否可聚合；其次通过功能性约束、资源约束选择可聚合物理节点集合，实现负载均衡，提高SFC可靠性；然后通过跳数约束进行优化，进一步筛选可聚合物理节点集合以降低SFC的端到端时延；最后通过节点可用资源、节点度以及与原节点跳数指标进行降序排列，取最大值物理节点部署VNF。SFC的路由选择，采用K-最短路径算法。仿真实验表明，该文所提算法提高了请求接受率、长期平均收益开销比，增强了SFC可靠性，降低了端到端时延，减小了平均带宽开销。Abstract: For the high reliability and low delay service requirements of 5G network, a Delay and Reliability Optimization of Service Function Chain (SFC) Deployment (DROSD) method is proposed. Without reservation of redundant resources, firstly, the function mutually exclusive constraints are used to determine whether the adjacent Virtual Network Function (VNF) in SFC can be combined; Secondly, functional constraints and resource constraints are used to select combinatorial physical node set to achieve load balancing and improve the reliability of SFC; Thirdly, the end-to-end delay of SFC is reduced by hop number constraints, and finally the VNF is deployed by the physical node with the maximum value, which is arranged in descending order through the available resources, node degree and hops from the original node. The routing of SFC adopts K-shortest path algorithm. The simulation results show that the proposed algorithm improves the request acceptance rate and the long-term average ratio of revenue to cost, enhances the reliability of SFC, reduces the end-to-end delay, and reduces the average bandwidth cost.
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表 1 算法1：DROSD算法VNF部署流程
输入：${G^{\rm{s}}}$, $G_{\rm{v}}^g$ 输出：VNF_Embedding_List 对于$G_{\rm{v}}^g$中的${\rm{SF}}{{\rm{C}}_g}$， (1) 获取${\rm{SF}}{{\rm{C}}_g}$的VNF数目K for k=1:K-1 利用深度优先，对可靠性进行优化，判断相邻VNF是否满
足功能互斥约束if $P_{k,k + 1}^g = 0$ 对时延进行优化，利用算法2选取可部署物理节点集合
${M_1}$, ${M_2}$(2) if ${M_1}{\rm{ = } }\varnothing$ 部署失败 else if ${M_2}{\rm{ = } }\varnothing$ 部署失败 else 计算节点$Im$值，取最大值节点部署${f_k}$ end end else 对可靠性和时延进行优化，利用算法3选取可聚合物理节点集合$M_1'$, $M_2'$ if $M_1'{\rm{ = } }\varnothing$ 聚合失败 返回步骤(2) else if $M_2'{\rm{ = } }\varnothing$ 聚合失败 返回步骤(2) else 计算节点$Im$值，取最大值节点部署${f_k}$, ${f_{k{\rm{ + }}1}}$ end end end 
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表 2 算法2：可部署物理节点选取流程
任意 ${f_k}$, ${w_i}$ for 未部署${\rm{SF}}{{\rm{C}}_g}$中VNF的所有物理节点$N_1^{\rm{s}}$ 选择满足功能性约束的物理节点 if $F_n^{{f_k},{w_i}}{\rm{ = }}1$ 选择满足资源约束的物理节点，实现负载均衡。 if $C_{n,{\rm{ava} } }^{\rm{s}} \ge C_{g,k}^{\rm{v}}{\rm{\& \& } }F_{n,{\rm{ava} } }^s \ge F_{g,k}^{\rm{v}}{\rm{\& \& } }{h_{ {S_1},n} } \le {h_0}$ $n \in {M_1}$ 利用与目的节点的跳数约束进一步优化时延 if ${h_{ {S_1},{D_g} } } \ge {h_{n,{D_g} } }$ $n \in {M_2}$ end end end end
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表 3 算法3：可聚合物理节点选取流程
任意${f_k}$, ${w_i}$和${f_{k{\rm{ + }}1}},{w_j}$ for 未部署${\rm{SF}}{{\rm{C}}_g}$中VNF的所有物理节点$N_1^{\rm{s}}$ 对可靠性进行优化，选择满足${f_k}$，${f_{k{\rm{ + }}1}}$功能性约束的物理节点 if $F_n^{{f_k},{w_i}}{\rm{ = }}1{\rm{\& \& }}F_n^{{f_{k{\rm{ + }}1}},{w_j}}{\rm{ = }}1$ 选择满足资源约束的物理节点，实现负载均衡。 if $C_{n,{\rm{ava} } }^{\rm{s} } \ge (C_{g,k}^{\rm{v} } \!+\! C_{g,k{\rm{ + } }1}^{\rm{v} }){\rm{\& \& } }F_{n,{\rm{ava} } }^{\rm{s} } \ge (F_{g,k}^{\rm{v}} \!+\! F_{g,k + 1}^{\rm{v} }){\rm{\& \& } }$
$ {h_{ {S_1},n} } \le {h_1} $$n \in M_1'$ 利用与目的节点的跳数约束进一步优化时延 if ${h_{ {S_1},D} } \ge {h_{n,D} }$ $n \in M_2'$ end end end end
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