Improving the Scalability of Deep Reinforcement Learning-Based Routing with Control on Partial Nodes

Abstract

Machine Learning (ML)-based routing optimization has been proposed to optimize the performance of flow routing for future networks, such as Software-Defined Networks (SDNs). However, existing studies are either hard to converge for large networks or vulnerable to topology changes. In this paper, we propose SINET, a scalable and intelligent network control framework for routing optimization. To improve the robustness and scalability, SINET selects several critical routing nodes to be directly controlled by a Deep Reinforcement Learning (DRL) agent, which dynamically generates routing policy to optimize network performance. Simulation results show that SINET can reduce the average flow completion time by at least 32% for a network with 82 nodes and exhibit better robustness against minor topology changes, compared to other DRL-based schemes.