<![CDATA[Software-Defined Networking (SDN) offers revolutionary flexibility and centralized management, yet ensuring reliable Quality of Service (QoS) for various applications remains a primary challenge. Although extensive research on QoS in SDN has been published, the proposed architectures, methods, and evaluation metrics are often disparate and complex. Consequently, A discernible gap exists in the current literature regarding a comprehensive survey of the research landscape for QoS in SDN. This literature review aims to identify and analyze the research trends, architectures, methods, and metrics related to QoS in SDN published between 2020 and 2025. Based on inclusion and exclusion criteria, 80 studies on QoS in SDN were identified. The analysis reveals four main research topics: Resource Management (45%), QoS-aware Routing (30%), Traffic Classification (20%), and QoS Security (5%). The majority of studies (70%) utilize simulation environments such as Mininet, while 30% employ physical testbeds In terms of methodology, it was found that mathematical optimization approaches such as Mixed-Integer Linear Programming (MILP) are still the most frequently implemented. However, there is a very clear trend of increasing proposals for Machine Learning (ML)-based methods, particularly Reinforcement Learning, as a solution for dynamic QoS management. This review provides a holistic view for researchers and practitioners to understand the current state and future direction of QoS research in SDN environments.]]>
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