Emerging network applications ranging from video streaming to virtual/augmented reality need to provide stringent quality-of-service (QoS) guarantees in complex and dynamic environments with shared resources. A promising approach to meeting these requirements is to automate complex network operations and create self-adjusting networks. These networks should automatically gather contextual information, analyze how to efficiently ensure QoS requirements, and adapt accordingly. This paper presents ReactNET, a self-adjusting networked system designed to achieve this vision by leveraging emerging network programmability and machine learning techniques. Programmability empowers ReactNET by providing fine-grained telemetry information, while machine learning-based classification techniques enable the system to learn and adjust the network to changing conditions. Our preliminary implementation of ReactNET in P4 and Python demonstrates its effectiveness in video streaming applications.

翻译：新兴网络应用（从视频流到虚拟/增强现实）需要在资源共享的复杂动态环境中提供严格的服务质量（QoS）保障。满足这些需求的一种有效途径是实现网络操作的自动化并构建自适应网络。这类网络应能自动收集环境信息，分析如何高效满足QoS要求，并据此进行动态调整。本文提出ReactNET——一种基于新兴网络可编程性与机器学习技术实现该愿景的自适应网络系统。可编程性通过提供细粒度遥测信息为ReactNET赋能，而基于机器学习的分类技术使系统能够学习并适应不断变化的网络条件。我们在P4和Python中对ReactNET的初步实现验证了其在视频流应用中的有效性。