A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization

TL;DR

This paper presents a deep-reinforcement learning agent that optimizes network routing in software-defined networking, adapting to traffic conditions to reduce delay and outperform traditional methods.

Contribution

The paper introduces a novel deep-reinforcement learning approach for routing optimization in SDN, demonstrating adaptive capabilities and operational benefits.

Findings

Significant reduction in network delay

Outperforms traditional optimization algorithms

Effective adaptation to traffic conditions

Abstract

In this paper we design and evaluate a Deep-Reinforcement Learning agent that optimizes routing. Our agent adapts automatically to current traffic conditions and proposes tailored configurations that attempt to minimize the network delay. Experiments show very promising performance. Moreover, this approach provides important operational advantages with respect to traditional optimization algorithms.