SDN-based Network Traffic Classification using Deep Reinforcement Learning
sifeddine salmi, Miloud Bagaa, Messaoud Ahmed Ouameur, Oussama Bekkouche, Adlen Ksentini · 2024
Software-Defined Networking (SDN) has emerged as a transformative technology that revolutionizes network management and architecture by providing unparalleled flexibility and control over data traffic flows. This flexibility is increasingly crucial in managing the complex demands of modern networks, whereby efficient traffic management is essential for mitigating congestion and enhancing operational efficiency. This paper introduces a novel traffic management model that employs Deep Reinforcement Learning (DRL) to transcend the conventional limitations typically associated with routing strategies that prioritize the shortest path or make non-optimal decisions when forwarding the traffic between different peers. Our model not only reduces overall network congestion but also aims to minimize bandwidth usage and enhance routing mechanisms within SDN environments. By incorporating DRL-based load balancing mechanisms, the model intelligently redistributes traffic across multiple pathways, shifting the focus from proximity to efficiency. This strategic redistribution prioritizes routes that optimize both, transmission time and network performance, rather than merely the shortest path. Moreover, the integration of DRL allows for real-time decision-making, enabling our system to dynamically adapt to changing traffic conditions and user demands. This capability is instrumental in significantly reducing transmission times and improving the overall efficiency of traffic flow across the network. Our findings highlight the substantial benefits of integrating SDN with advanced DRL techniques, offering a pioneering perspective on traffic routing within SDN networks. We evaluated the proposed framework via simulations and the obtained results demonstrated the efficiency of our solution compared to the baseline approaches.