Unleashing Automated Congestion Control Customization in the Wild
This work addresses the problem of optimizing user experience for Internet services like streaming and gaming by providing an incremental improvement through automated customization of congestion control.
The paper tackles the challenge of designing congestion control algorithms that perform well across diverse applications and networks by developing a system that automatically customizes congestion control logic to specific service needs and network conditions, demonstrating performance benefits in case studies such as streaming, gaming, and connected cars.
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.