Unleashing Automated Congestion Control Customization in the Wild
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 2 of its 18 references that are in the map. First posted on arXiv 18 May 2025.
Builds on
2 of 18 references.
References · 2
- 2019Introduction to Online Convex Optimizationcited by 803
- 2019Introduction to Multi-Armed Banditscited by 481
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Citing papers per year: 2026: 1.
1 paper.