Robust Path Selection in Software-defined WANs using Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based method for robust path selection in SD-WANs, reducing routing update overhead and improving link utilization compared to traditional traffic engineering schemes.

Contribution

The paper presents a novel data-driven algorithm leveraging past network data to select robust paths, balancing update overhead and network performance.

Findings

Reduces link utilization by 40% compared to traditional TE schemes.

Achieves 25% higher link utilization than schemes ignoring update overhead.

Demonstrates effectiveness through extensive simulations on real network topologies.

Abstract

In the context of an efficient network traffic engineering process where the network continuously measures a new traffic matrix and updates the set of paths in the network, an automated process is required to quickly and efficiently identify when and what set of paths should be used. Unfortunately, the burden of finding the optimal solution for the network updating process in each given time interval is high since the computation complexity of optimization approaches using linear programming increases significantly as the size of the network increases. In this paper, we use deep reinforcement learning to derive a data-driven algorithm that does the path selection in the network considering the overhead of route computation and path updates. Our proposed scheme leverages information about past network behavior to identify a set of robust paths to be used for multiple future time intervals to avoid the overhead of updating the forwarding behavior of routers frequently. We compare the results of our approach to other traffic engineering solutions through extensive simulations across real network topologies. Our results demonstrate that our scheme fares well by a factor of 40% with respect to reducing link utilization compared to traditional TE schemes such as ECMP. Our scheme provides a slightly higher link utilization (around 25%) compared to schemes that only minimize link utilization and do not care about path updating overhead.