论文标题
通过梯度下降的快速交通工程，并通过学习的可区分路由
Fast Traffic Engineering by Gradient Descent with Learned Differentiable Routing
论文作者
论文摘要
元应用，伸缩式或云计算等新兴应用需要对网络（例如超可靠的低潜伏期）的运营需求越来越复杂。同样，不断努力的流量动态将需要网络控制机制，这些机制可以在短时间内（例如，次级）运行。在这种情况下，流量工程（TE）是根据某些性能目标有效控制网络流量的关键组件（例如，将网络拥塞最小化）。 本文介绍了Backprop（RBB）的路由，这是一种基于图神经网络（GNN）和可区分编程的新型TE方法。借助其内部GNN模型，RBB构建了目标TE问题的端到端可区分功能（MinMaxLoad）。这可以通过梯度下降快速优化。在我们的评估中，我们显示了RBB优化基于OSPF的路由的潜力（$ \ $ \ $ 25 \％相对于默认的OSPF配置的改进）。此外，我们测试了RBB作为计算密集型TE求解器的初始化的潜力。实验结果表明，加速了这种类型的求解器并实现有效的在线TE优化的前景。
Emerging applications such as the metaverse, telesurgery or cloud computing require increasingly complex operational demands on networks (e.g., ultra-reliable low latency). Likewise, the ever-faster traffic dynamics will demand network control mechanisms that can operate at short timescales (e.g., sub-minute). In this context, Traffic Engineering (TE) is a key component to efficiently control network traffic according to some performance goals (e.g., minimize network congestion). This paper presents Routing By Backprop (RBB), a novel TE method based on Graph Neural Networks (GNN) and differentiable programming. Thanks to its internal GNN model, RBB builds an end-to-end differentiable function of the target TE problem (MinMaxLoad). This enables fast TE optimization via gradient descent. In our evaluation, we show the potential of RBB to optimize OSPF-based routing ($\approx$25\% of improvement with respect to default OSPF configurations). Moreover, we test the potential of RBB as an initializer of computationally-intensive TE solvers. The experimental results show promising prospects for accelerating this type of solvers and achieving efficient online TE optimization.
