In the future, it is anticipated that software-defined networking (SDN) will become the preferred platform for deploying diverse networks. Compared to traditional networks, SDN separates the control and data planes for efficient domain-wide traffic routing and management. The controllers in the control plane are responsible for programming data plane forwarding devices, while the top layer, the application plane, enforces policies and programs the network. The different levels of the SDN use interfaces for communication. However, SDN faces challenges with traffic distribution, such as load imbalance, which can negatively affect the network performance. Consequently, developers have developed various SDN load-balancing solutions to enhance SDN effectiveness. In addition, researchers are considering the potential of implementing some artificial intelligence (AI) approaches into SDN to improve network resource usage and overall performance due to the fast growth of the AI field. This survey focuses on the following: Firstly, analyzing the SDN architecture and investigating the problem of load balancing in SDN. Secondly, categorizing AI-based load balancing methods and thoroughly assessing these mechanisms from various perspectives, such as the algorithm/technique employed, the tackled problem, and their strengths and weaknesses. Thirdly, summarizing the metrics utilized to measure the effectiveness of these techniques. Finally, identifying the trends and challenges of AI-based load balancing for future research.

翻译：未来，软件定义网络（SDN）有望成为部署多样化网络的首选平台。与传统网络相比，SDN通过分离控制平面与数据平面，实现了高效的域级流量路由与管理。控制平面中的控制器负责编程数据平面转发设备，而顶层应用平面则执行策略并对网络进行编程。SDN的不同层级通过接口进行通信。然而，SDN面临流量分布方面的挑战（如负载不均衡），这可能对网络性能产生负面影响。为此，开发者已提出多种SDN负载均衡方案以提升其效能。此外，随着人工智能（AI）领域的快速发展，研究者正考虑将部分AI方法引入SDN，以优化网络资源利用率和整体性能。本综述聚焦以下内容：首先，分析SDN架构并探究SDN中的负载均衡问题；其次，对基于AI的负载均衡方法进行分类，并从算法/技术、所解决问题及其优缺点等多维度深入评估这些机制；再次，总结用于衡量这些技术有效性的指标；最后，指出基于AI的负载均衡的未来研究趋势与挑战。