## 2 Related Work  
Some published works related to SDN networks, latency management, and other quality of service (QoS) parameters, which are closely related to this study, can be describedas follows. In [5], by using an artificial intelligence approach applied to software-defined networking architecture, challenges in underwater things (IoUT), such as load balancing and QoS, are addressed. The study aims to enhance network performance, flexibility, and scalability, using a balancing strategy multi-controller (CASM). Additionally, an adaptive routing protocol (SQAR) using reinforcement learning is proposed to select paths for different services while optimizing network QoS. The results show that the balancing strategy improves migration and achieves efficient load-balancing, benefiting SQAR directly. SQAR has highlights in terms of energy efficiency, convergence, and QoS when compared to other established protocols. Overall, the proposed framework achieves significant and effective rates in both QoS and load balancing. In [6], a methodology is introduced to enable the dynamic creation and adjustment of network segments to meet the varying needs of different user applications. This approach focuses on efficiently allocating resources, such as bandwidth and energy, ensuring optimal performance and adequate QoS in future 5G networks. In [7], two quadratic programming models and their linear equivalent counterparts are proposed. These models address the problem of user assignment in a software-defined network while aiming to minimize the connectivity costs and connection latencies between controllers and switches. The results show that the linear models have shorter processing times than the quadratic models for obtaining optimal solutions. The study compares user connection strategies, and the findings suggest that users should connect to switches. In [8], by using reinforcement learning with constraints, a model is proposed to optimize resource user segmentation while also meeting the requirements of existing users and applications in the network. In [9], methods such as Controller adaption and migration decision (CAMD) and dynamic and adaptive load-balancing (DALB) are used to address load imbalance in an SDN network. The results show that CAMD is the most efficient method. This algorithm utilizes the inherent variance in controller load status to identify under-utilized controllers, optimizing the migration process to balance network load. The performance metrics considered include response time, migration space, controller throughput, and packet loss. CAMD outperforms controller adaption and migration decisions, with average improvements of 7.4% in controller throughput, 6.4% in packet loss, 5.6% in migration space, and 5.7% in response time. In [10], the authors analyze an overview of the evolution of SDN controllers. Here, the authors develop advancements in architecture, protocol, and key features. In [11; 12], the problem of latency minimization in an SDN is addressed. The first article focuses on the controller placement problem in a software-defined network to minimize the maximum latency for switches and controllers as well as communication between controllers. Additionally, it considers reducing the number of controllers needed for network operation, taking into account a fixed installation cost for each located controller. The second study proposes mathematical models to minimize latency in SDN networks. Both articles [11; 12] aim to improve performance and quality of service by reducing network latency and managing messages between each pair of nodes for the existing arcs, considering the controllers. In [13], an approach for the cost-effective and efficient placement of switches and controllers in a hybrid SDN is proposed. Elements of QoS and implementation costs are considered to determine the optimal placement, maximizing QoS, and minimizing infrastructure deployment costs. In [14], the growing energy consumption of communication technologies, such as 5G and beyond, is addressed.