An Implementation of Deep Reinforcement Learning‐Based Routing Framework for Open‐Network Operating System‐Controlled and Mininet‐Emulated Software‐Defined Networking
Marwa Kandil Mohammed, Mohamad Khattar Awad, Eiman Mohammed Alotaibi, Reza Malek Mohammadi · IET Networks · 2025
ABSTRACT Coping with the unprecedented surge in traffic volume necessitates a profound overhaul of traditional networking architectures. In response, software‐defined networking (SDN) has emerged as a groundbreaking architecture that separates the control plane from the data plane, relocating it to a more computationally capable central controller. This paradigm shift paves the way for integrating recent advancements in reinforcement learning (RL) for traffic engineering and routing. This paper presents a systematic guide to implementing this integration in Java‐based, open‐source, open‐network operating system (ONOS) SDN controllers. The control plane implementation in ONOS and data plane implementation in Mininet constitute a holistic SDN framework for evaluating the performance of RL‐based traffic engineering and routing schemes. Furthermore, we implement a direct‐policy transfer algorithm to enhance the RL agent's reaction time to link failures in the network topology. Considering end‐to‐end delay, throughput, and packet‐loss ratio as our performance evaluation metrics, we compare and contrast the performance of four existing schemes.