Dynamic Predictive Feedback Mechanism for Intelligent Bandwidth Control in Future SDN Networks

Future programmable networks such as 5G/6G and large-scale IoT deployments demand dynamic and intelligent bandwidth control mechanisms to ensure stable Quality of Service (QoS) under highly variable traffic conditions. Conventional queue-based schedulers and emerging machine learning techniques still struggle with slow reaction to congestion, unstable fairness, and high computational costs. To address these challenges, this paper proposes a Dynamic Predictive Feedback (DPF) mechanism that integrates clustered-LSTM based short-term traffic prediction with meta-control driven adaptive bandwidth adjustment in a Software-Defined Networking (SDN) architecture. The prediction module proactively estimates future queue depth and arrival rates using in-band network telemetry (INT), while the feedback controller continuously adjusts scheduling weights based on congestion risk and fairness metrics. Extensive emulation experiments conducted under Static, Bursty IoT, Mixed, and Stress workloads show that DPF consistently outperforms state-of-the-art solutions, including A-WFQ and DRL-based schedulers, achieving up to 32% higher throughput, up to 40% lower latency, and 10–12% lower CPU and memory usage. Moreover, DPF demonstrates strong fairness (Jain’s Index ≥ 0.96), high adaptability, and minimal performance variance across scenarios. These results confirm that DPF is a scalable and resource-efficient solution capable of supporting the demands of future programmable, 5G/6G-ready network infrastructures.