Abstract
Abstract In prospective advancements, it is anticipated that software-defined networking (SDN) will establish itself as the principal framework for the implementation of heterogeneous networks. Unlike traditional networking paradigms, SDN differentiates the control and data planes, thereby enabling enhanced routing and management of traffic across various domains. The controllers located within the control plane are responsible for programming the forwarding devices situated in the data plane, whereas the highest layer, referred to as the application plane, enforces regulatory policies and coordinates network programming. Interfacing mechanisms are utilized across the different layers of SDN to ensure effective communication. Nonetheless, SDN faces challenges related to traffic distribution, such as load imbalance, which could negatively affect overall network performance. In response, developers have formulated a range of SDN load-balancing solutions intended to improve the efficiency of SDN. Furthermore, owing to the swift advancements in the domain of artificial intelligence (AI), researchers are investigating the viability of incorporating AI methodologies into SDN to optimize resource utilization and enhance overall performance. This survey is organized as follows: First, it provides an analysis of the SDN architecture and investigates the load balancing challenges present within SDN. Second, it classifies AI-driven load balancing strategies and critically assesses these mechanisms from multiple perspectives, including the algorithms/techniques utilized, the particular issues addressed, and their corresponding advantages and disadvantages. Third, it consolidates the metrics used to evaluate the effectiveness of these techniques. Finally, it identifies the emerging trends and challenges associated with AI-enhanced load balancing for future research endeavors.
Published Version
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