论文标题
Routenet-Erlang：用于网络性能评估的图形神经网络
RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation
论文作者
论文摘要
网络建模是网络研究，设计和操作中的基本工具。可以说，建模最流行的方法是排队理论（QT）。它的主要限制是，它对数据包到达过程施加了强有力的假设，该过程通常不存在于实际网络中。在深度学习领域，图形神经网络（GNN）已成为一种新技术，用于构建可以学习复杂和非线性行为的数据驱动模型。在本文中，我们提出\ emph {Routenet-erlang}，这是一种旨在建模计算机网络的开拓性GNN架构。 Routenet-Erlang支持复杂的流量模型，多标题调度策略，路由策略，并且可以在培训阶段未观察到的网络中提供准确的估计。我们根据最先进的QT模型对路由搜索基准进行了基准，我们的结果表明，它在所有网络方案中都表现优于QT。
Network modeling is a fundamental tool in network research, design, and operation. Arguably the most popular method for modeling is Queuing Theory (QT). Its main limitation is that it imposes strong assumptions on the packet arrival process, which typically do not hold in real networks. In the field of Deep Learning, Graph Neural Networks (GNN) have emerged as a new technique to build data-driven models that can learn complex and non-linear behavior. In this paper, we present \emph{RouteNet-Erlang}, a pioneering GNN architecture designed to model computer networks. RouteNet-Erlang supports complex traffic models, multi-queue scheduling policies, routing policies and can provide accurate estimates in networks not seen in the training phase. We benchmark RouteNet-Erlang against a state-of-the-art QT model, and our results show that it outperforms QT in all the network scenarios.
