  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.
