Adaptive Resource Management in Software-Defined Networks for IoT Ecosystems

The proliferation of Internet of Things (IoT) devices has imposed some challenges on traditional network management systems, necessitating diverse approaches for efficient resource allocation. In this paper, we present a framework for adaptive resource management in Software-Defined Networks (SDNs) tailored to IoT ecosystems. Leveraging the inherent flexibility and programmability of SDNs, our methodology integrates reinforcement learning techniques to dynamically allocate network resources based on real-time demands of IoT applications. Our approach introduces a dual-stage Deep Q-Network (DQN) architecture, enhancing stability and accuracy in learning optimal resource allocation policies. This dual-stage DQN, combined with a multi-agent reinforcement learning strategy, maximizes network performance while ensuring fairness and Quality of Service (QoS). The proposed system is evaluated in a simulated IoT environment using the OpenDaylight (ODL) SDN controller. The experimental results demonstrate a significant improvement in key performance metrics, including increased throughput, reduced latency, and decreased energy consumption compared to traditional methods. These enhancements highlight the potential of our adaptive resource management framework in addressing the complex requirements of modern IoT ecosystems.