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 as well as in the number of network applications. To enable the deployment of emergent network applications (e.g., Vehicular networks, Internet of Things), existing Traffic Engineering (TE) solutions must be able to achieve high performance real-time network operation. In addition, TE solutions must be able to adapt to dynamic scenarios (e.g., changes in the traffic matrix or topology link failures). However, current TE technologies rely on hand-crafted heuristics or computationally expensive solvers, which are not suitable for highly dynamic TE scenarios. In this paper we propose Enero, an efficient real-time TE engine. Enero is based on a two-stage optimization process. In the first one, it leverages Deep Reinforcement Learning (DRL) to optimize the routing configuration by generating a long-term TE strategy. We integrated a Graph Neural Network (GNN) into the DRL agent to enable efficient TE on dynamic networks. In the second stage, Enero uses a Local Search algorithm to improve DRL's solution without adding computational overhead to the optimization process. Enero offers a lower bound in performance, enabling the network operator to know the worst-case performance of the DRL agent. We believe that the lower bound in performance will lighten the path of deploying DRL-based solutions in real-world network scenarios. 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.
翻译:广域网(WAN)是当今社会的关键基础设施。 在过去的几年中,广域网在网络流量和网络应用程序数量上都出现了显著增长。为了能够部署突发网络应用程序(例如,电视网络、物联网网网),现有的交通工程(TE)解决方案必须能够实现高性能实时网络操作。此外,TE解决方案必须能够适应动态情景(例如,交通矩阵的变化或地形介质连接失败)。然而,目前的TE技术依赖于手工艺超常或计算成本昂贵的解决方案,不适合高度动态的TE情景。在这个文件中,我们建议使用高效实时电动引擎。 Enero 现有的交通工程(TE) 解决方案必须能够实现高性能实时网络运行。首先,它必须利用深加力学习(DRL), 以产生长期的TE战略, 将基于电磁基神经网络(GNNNN) 运行到 DRL 代理器,以便能够在动态网络上实现高效的TE。在第二个阶段,Eneroroad, 快速智能网络的运行流程将显示Enteralal developal commal commal commal commal roisal commal dal 提供 。在最低的运行中将改进Enal deal deal develristral deal commal commal commal commal dal commal commaldaldal commo comm 将改进到Estrol commaldaldal 。