A Demand-aware Networked System Using Telemetry and ML with ReactNET

TL;DR

ReactNET is a self-adjusting network system that uses telemetry and machine learning to dynamically optimize QoS for applications like video streaming in complex environments.

Contribution

It introduces ReactNET, a novel system combining network programmability and ML for automated, adaptive network management.

Findings

Effective in improving QoS for video streaming

Utilizes fine-grained telemetry for better network insights

Demonstrates preliminary success in P4 and Python implementations

Abstract

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.