Multi-controller Placement Strategy Based on Latency and Load in Software Defined Network
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摘要: 在多控制器管理的软件定义网络(SDN)中，时延和负载是控制器放置问题(CPP)要考虑的重要因素。该文以降低控制器之间的传播时延、流请求的传播时延和排队时延、均衡控制器间负载为目标，提出一种控制器放置及动态调整的策略，其中包括用于初始控制器放置的负载均衡算法(BCRA)和遗传算法(GA)，用于动态调整控制器负载的在线调整算法(ADOA)。以上算法均考虑网络连通性。仿真结果表明：在初始控制器放置时，在保证流请求的传播时延、排队时延和控制器传播时延较低的情况下，BCRA部署在中小型网络中时，其负载均衡性能与GA相近且优于k-center和k-means算法；GA部署在大型网络中时，与BCRA, k-center和k-means算法相比，使得负载均衡率平均提高了49.7%。在动态情况下，与现有动态调整算法相比，ADOA可以保证较低排队时延和运行时间的同时，仍能使负载均衡参数小于1.54。Abstract: In Software Defined Networks (SDN), latency and load are important factors for Controller Placement Problem (CPP). To reduce the transmission latency between controllers, the propagation latency and queuing latency of flow requests, and balance the controller load, a strategy on how to place and adjust the controller is proposed. It mainly includes Genetic Algorithm (GA) and Balanced Control Region Algorithm (BCRA) which are used to place the initial controller and one Algorithm of Dynamic Online Adjustment (ADOA), that is an online adjusting algorithm in term dynamic controlling. The above algorithms are all based on the network connectivity. The simulation results show that in initial controller placement situation, under the premise of guaranteeing the lower propagation latency, queue latency and controller transmission latency of flow request, when BCRA is deployed in small and medium-sized networks, its load balancing performance is similar to that of GA and superior to k-center and k-means algorithm; When GA is deployed in large networks, compared with BCRA, k-center and k-means, the load balancing rate increases averagely 49.7%. In the dynamic situation, ADOA can guarantee lower queuing delay and running time, and can still make the load balance parameter less than 1.54.
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表 1 BCRA算法
算法1 BCRA算法 输入：图$G(V,E)$, ${\lambda _{i,j}}$, ${\rm{Cons}}$, ${\rm{C}}{{\rm{A}}_{{\rm{cons}}}}$, $C\;$//${\rm{C}}{{\rm{A}}_{{\rm{cons}}}}$, $C\;$都是空集 输出：$F$的值，控制器集合$C$和子网集合${\rm{C}}{{\rm{A}}_{{\rm{cons}}}}$ (1) 计算各个节点的度$D$; //步骤(1)～步骤(10)是网络划分阶段；
(2) 计算$k = \sum\nolimits_{j = 1}^n {{\lambda _j}} /{\rm{LCPmax}}$; //${\lambda _j}$表示第$j$个交换机流量，${\rm{LCPmax}}$表示最大负载；(3) while $\left| C\, \right| < k$ (4) 如果算法第1次执行，则选择度最大的节点${\rm{Cons}}$作为控制器节点；否则，从剩余节点中找到度最大的节点并组成集合，再从该集
合中选择距离当前控制器节点最近的点${\rm{Cons}}$作为新的控制器节点；(5) 将节点${\rm{Cons}}$加入控制器集合$C$； (6) while ${\rm{LC}}{{\rm{P}}_{{\rm{cons}}}} < {\mu _{{\rm{cons}}}}$//基于${\rm{Con}}$使用广度优先搜索方法构建树； (7) 选择离${\rm{Con}}$最近且未被分配的节点$j$，将其加入${\rm{C}}{{\rm{A}}_{{\rm{cons}}}}$； (8) end while (9) end while (10) 找到每个控制器对应的交换机集合$\left\{ {{\rm{C}}{{\rm{A}}_{{\rm{cons}}}}} \right\} ,{\rm{cons}} \in C\;$,计算$F\;$的值并记为${\rm{Fold}}$； (11) while ${\rm{BL}} - 1 > \xi $//$\xi $是一个极小值，步骤(11)—步骤(19)是子网调整阶段； (12) 找到负载最大的控制器${\rm{Cons}}$的子网，并计算子网内的边界交换机与相邻控制器之间的距离； (13) 预先计算将距离最小的交换机分配给相邻控制器后的$F$的值，并标记为${\rm{Fnew}}$； (14) if ${\rm{Fnew}} \le {\rm{Fold}}$ (15) 将距离最小的交换机分配给相邻控制器，并令${\rm{Fold = Fnew}}$；
(16) 更新交换机集合$\left\{ {{\rm{C}}{{\rm{A}}_{{\rm{cons}}}}} \right\}$并计算$\sum\nolimits_{j \in {\rm{C}}{{\rm{A}}_{{\rm{cons}}}}} {\lambda _{{\rm{cons}},j}^{}} $；(17) end if (18) $F = {\rm{Fold}}$； (19) end while (20) 输出$F,C,\left\{ {{\rm{C}}{{\rm{A}}_{{\rm{cons}}}}} \right\}$ 
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表 2 BLA算法
算法2 BLA算法 输入：图$G(V,E)$, ${\lambda _{i,j}}$, ${\mu _i}$, ${\rm{CA}}$, $C\,$//${\rm{CA}}$是空集 输出：适应度函数值$F$ (1) for $i \in C$ (2) 选择节点$j$，其满足离控制器$i$最近且未被标记；
(3) if $\sum\nolimits_{j \in {\rm{C}}{{\rm{A}}_i}} {{\lambda _{i,j}}} < {\mu _i}$(4) 将节点$j$加入${\rm{C}}{{\rm{A}}_i}$； (5) end if (6) end for (7) 找到每个控制器对应的交换机集合$\left\{ {{\rm{C}}{{\rm{A}}_i}} \right\} ,i \in C\,$，计算
$F\,$的值并记为${\rm{Fold}}$；(8) 接着调用BCRA的子网调整阶段算法；//即算法1的步
骤(11)—步骤(19)；(9) 输出$F$
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表 3 ADOA算法
算法3 ADOA算法 输入：${\rm{C}}{{\rm{A}}_i}$, ${\lambda _{i,j}}$, ${\mu _i}$, $C$, ${\rm{LC}}{{\rm{P}}_i}^{}$, $D$ 输出：${\rm{C}}{{\rm{A}}_{i,o}}$ (1) while ${\rm{LC}}{{\rm{P}}_i}$改变并且${\rm{T}}{{\rm{q}}_i} > {\rm{Tqmax}}$ (2) 选择当前子网中度最大的节点$o$作为从控制器； (3) for $j \in {\rm{C}}{{\rm{A}}_i}$ (4) 如果${\rm{LC}}{{\rm{P}}_o} > {\rm{LC}}{{\rm{P}}_i}$，则跳出当前循环并转入步骤2； (5) if 相对于控制器$i$，节点$j$离控制器$o$近； (6) 预先计算将$j$分配给$o$后的${\rm{T}}{{\rm{q}}_o}$； (7) 如果${\rm{T}}{{\rm{q}}_o} < {\rm{Tqmax}}$，则令$j \in {\rm{C}}{{\rm{A}}_{i,o}}$且$j \notin {\rm{C}}{{\rm{A}}_i}$； (8) end if (9) end for (10) end while (11) 输出${\rm{C}}{{\rm{A}}_{i,o}}$
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