ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning

TL;DR

Enero is a real-time WAN traffic engineering solution that combines deep reinforcement learning and local search to adapt efficiently to dynamic network conditions, ensuring high performance with low latency.

Contribution

The paper introduces Enero, a novel two-stage optimization method using DRL and local search for real-time WAN routing management under dynamic scenarios.

Findings

Operates in 4.5 seconds for topologies up to 100 edges

Effectively adapts to link failures and dynamic traffic changes

Outperforms traditional TE methods in real-world scenarios

Abstract

Wide Area Networks (WAN) are a key infrastructure in today's society. During the last years, WANs have seen a considerable increase in network's traffic and network applications, imposing new requirements on existing network technologies (e.g., low latency and high throughput). Consequently, Internet Service Providers (ISP) are under pressure to ensure the customer's Quality of Service and fulfill Service Level Agreements. Network operators leverage Traffic Engineering (TE) techniques to efficiently manage network's resources. However, WAN's traffic can drastically change during time and the connectivity can be affected due to external factors (e.g., link failures). Therefore, TE solutions must be able to adapt to dynamic scenarios in real-time. In this paper we propose Enero, an efficient real-time TE solution based on a two-stage optimization process. In the first one, Enero leverages Deep Reinforcement Learning (DRL) to optimize the routing configuration by generating a long-term TE strategy. To enable efficient operation over dynamic network scenarios (e.g., when link failures occur), we integrated a Graph Neural Network into the DRL agent. In the second stage, Enero uses a Local Search algorithm to improve DRL's solution without adding computational overhead to the optimization process. The experimental results indicate that Enero is able to operate in real-world dynamic network topologies in 4.5 seconds on average for topologies up to 100 edges.