Adaptive Reinforcement Learning Algorithms for Intelligent Resource Management in Software-Defined Networks

Resumen

Software-Defined Networking (SDN) has emerged as a transformative architecture that decouples the control plane from the data plane, offering unprecedented programmability, scalability, and flexibility in managing modern communication networks. However, the increasing heterogeneity of network traffic, rapid growth of latency-sensitive applications, and dynamic variations in user demands have intensified the complexity of efficient resource management in SDN environments. Traditional static or heuristic-driven resource allocation strategies often fail to adapt to rapidly changing network states, leading to congestion, sub-optimal flow scheduling, and inefficient bandwidth utilization. Reinforcement Learning (RL), with its ability to learn optimal actions through continuous interaction with the environment, presents a compelling solution to these challenges, but standard RL models still struggle with high-dimensional state spaces, delayed rewards, convergence instability, and limited generalization across diverse network conditions. This paper proposes an adaptive reinforcement learning framework tailored for intelligent resource management in SDN, integrating model-free RL, model-based RL, and deep reinforcement learning (DRL) mechanisms with adaptive feedback loops and network-aware learning strategies. The framework dynamically adjusts flow rules, bandwidth distribution, routing paths, and QoS policies by learning from real-time traffic patterns, system congestion levels, and predictive demand modeling. Comparative analysis demonstrates that adaptive RL algorithms significantly outperform static SDN controllers and classical optimization approaches in terms of throughput, latency minimization, fairness, and energy-efficient routing.