Unleashing Automated Congestion Control Customization in the Wild

TL;DR

This paper presents a system that automatically customizes congestion control algorithms to specific service needs and network conditions, improving performance across diverse Internet applications.

Contribution

It introduces a practical system leveraging online-learning based congestion control, with insights and modifications for real-world deployment across various applications.

Findings

Performance improvements in streaming, gaming, and connected cars

Successful deployment of customized congestion control logic

Lessons learned for real-world adaptation

Abstract

Congestion control (CC) crucially impacts user experience across Internet services like streaming, gaming, AR/VR, and connected cars. Traditionally, CC algorithm design seeks universal control rules that yield high performance across diverse application domains and networks. However, varying service needs and network conditions challenge this approach. We share operational experience with a system that automatically customizes congestion control logic to service needs and network conditions. We discuss design, deployment challenges, and solutions, highlighting performance benefits through case studies in streaming, gaming, connected cars, and more. Our system leverages PCC Vivace, an online-learning based congestion control protocol developed by researchers. Hence, along with insights from customizing congestion control, we also discuss lessons learned and modifications made to adapt PCC Vivace for real-world deployment.