In the context of wide area networks (WANs) and software defined networking (SDN), reducing the communication delays experienced by the network devices is an important challenge whose solution requires a careful placement of controllers to decrease the end-to-end latencies. Although the majority of studies focusing on the controller placement problem (CPP) only consider the switch–controller propagation delays and inter-controller latencies, the controllers’ capacity needs to be addressed for lowering the end-to-end delays. While the controllers with a variety of processing rates, number of ports, and costs are available in the market, Internet Service Providers (ISPs) need to consider the affordability and capability of compensating their needs by maximizing the use of their networks’ resources. To propose a solution for this important problem, we consider the Knapsack 0–1problem and formulate the Garter Snake Optimization Capacitated Controller Placement Problem (GSOCCPP), a meta-heuristic algorithm, with new iterations and temperate mating conditions to solve the CPP. This algorithm uses a reasonable amount of computation time to obtain the minimum delays. A number of Topology-Zoo datasets are analyzed with a variety of small to large scale data plane nodes to evaluate the GSOCCPP algorithm based on different controllers’ capacities. The simulation results demonstrate that, in addition to outperforming similar meta-heuristic and clustering algorithms such as the Firefly Algorithm, Particle Swarm Optimization, and the k-means++, our newly proposed GSOCCPP algorithm is successful in achieving the lowest execution time among the analyzed algorithms. Furthermore, this proposed solution has a more efficient memory consumption compared to other algorithms for controller placement in different network topologies.

在广域网（WAN）和软件定义的网络（SDN）的背景下，减少网络设备遇到的通信延迟是一项重要的挑战，其解决方案需要仔细放置控制器以减少端到端延迟。尽管大多数针对控制器放置问题（CPP）的研究都只考虑了开关控制器传播延迟和控制器间延迟，但仍需要解决控制器的能力以降低端到端延迟。尽管市场上有各种处理速率，端口数量和成本的控制器，但Internet服务提供商（ISP）需要考虑通过最大程度地利用网络资源来补偿其需求的可承受性和能力。要为这个重要问题提出解决方案，我们考虑背包问题0–1的问题，并制定了一种基于元启发式算法的Garter Snake优化容量控制器布局问题（GSOCCPP），并采用了新的迭代方法和温带交配条件来求解CPP。该算法使用合理的计算时间来获得最小延迟。分析了许多Topology-Zoo数据集，其中包含各种规模较小的数据平面节点，以根据不同控制器的能力评估GSOCCPP算法。仿真结果表明，除了性能优于类似的元启发式算法和聚类算法（如Firefly算法，粒子群优化算法和 通过新的迭代和温和的交配条件来解决CPP。该算法使用合理的计算时间来获得最小延迟。分析了许多Topology-Zoo数据集，其中包含各种规模较小的数据平面节点，以根据不同控制器的能力评估GSOCCPP算法。仿真结果表明，除了性能优于类似的元启发式算法和聚类算法（如Firefly算法，粒子群优化算法和 通过新的迭代和温和的交配条件来解决CPP。该算法使用合理的计算时间来获得最小延迟。分析了许多Topology-Zoo数据集，其中包含各种规模较小的数据平面节点，以根据不同控制器的能力评估GSOCCPP算法。仿真结果表明，除了性能优于类似的元启发式算法和聚类算法（如Firefly算法，粒子群优化算法和k -means ++，我们新提出的GSOCCPP算法成功地实现了分析算法中最短的执行时间。此外，与用于在不同网络拓扑中放置控制器的其他算法相比，该提议的解决方案具有更高的内存消耗。