Systematic Analysis of Quality-of-Service Optimization Strategies in Software-Defined Network Environments

Software-Defined Networking (SDN) heralds the future of networks with its programmability, centralized control, and flexibility that could easily surpass traditional networks in managing Quality-of-Service (QoS). This literature review, adhering to PRISMA 2020 guidelines, selects 62 studies from 1,142 initially found and published between 2016 and mid-2025, all of them peer-reviewed articles and obtained from four databases, IEEE Xplore, Scopus, Web of Science, and ScienceDirect. The review highlights six chief SDN-based research QoS enhancement methods: the machine, deep, and reinforcement learning methods; dynamic queuing and scheduling; controller positioning and load balancing; policy- or intent-based frameworks; telemetry-based closed-loop control; and combined SDN and legacy integration. The majority of the studies explore both data and control planes simultaneously. Improved performance parameters such as latency, throughput, jitter, and packet loss have been reported in the outcome, however, these results mostly come from small-scale testbeds, simulations, and synthetic workloads with very limited real-world deployment, security evaluation, energy assessment, or hardware-based validation. In any case, SDN is still considered to be an option for carrier-grade QoS optimization but its operational suitability is still not clear. Future research should focus on reproducible, realistic, and operationally grounded assessments to close the gap between theoretical promise and large-scale industrial implementation