The demand of low latency applications has fostered interest in edge computing, a recent paradigm in which data is processed locally, at the edge of the network. The challenge of delivering services with low-latency and high bandwidth requirements has seen the flourishing of Software-Defined Networking (SDN) solutions that utilize ad-hoc data-driven statistical learning solutions to dynamically steer edge computing resources. In this paper, we propose RoPE, an architecture that adapts the routing strategy of the underlying edge network based on future available bandwidth. The bandwidth prediction method is a policy that we adjust dynamically based on the required time-to-solution and on the available data. An SDN controller keeps track of past link loads and takes a new route if the current path is predicted to be congested. We tested RoPE on different use case applications comparing different well-known prediction policies. Our evaluation results demonstrate that our adaptive solution outperforms other ad-hoc routing solutions and edge-based applications, in turn, benefit from adaptive routing, as long as the prediction is accurate and easy to obtain.

中文翻译：

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RoPE：边缘自适应数据驱动路由预测架构

低延迟应用程序的需求激发了对边缘计算的兴趣，边缘计算是在网络边缘本地处理数据的最新范例。提供具有低延迟和高带宽要求的服务的挑战见证了软件定义网络 (SDN) 解决方案的蓬勃发展，这些解决方案利用临时数据驱动的统计学习解决方案来动态引导边缘计算资源。在本文中，我们提出了 RoPE，这是一种基于未来可用带宽调整底层边缘网络路由策略的架构。带宽预测方法是我们根据所需的解决时间和可用数据动态调整的策略。SDN 控制器会跟踪过去的链路负载，并在预测当前路径拥塞时采用新路由。我们在不同的用例应用程序上测试了 RoPE，比较了不同的知名预测策略。我们的评估结果表明，只要预测准确且易于获得，我们的自适应解决方案优于其他 ad-hoc 路由解决方案和基于边缘的应用程序，反过来，也受益于自适应路由。