An Implementation of Deep Reinforcement Learning-Based Routing Framework for Open-Network Operating System-Controlled and Mininet-Emulated Software-Defined Networking

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.