Unleashing Automated Congestion Control Customization in the Wild

2025-05-18

AI Summary

Traditional congestion control algorithms struggle to simultaneously satisfy heterogeneous service requirements—such as those of streaming media, cloud gaming, and vehicular networks—and adapt to dynamic network conditions. This paper proposes the first congestion control auto-customization system designed for real-world operational scenarios. Built upon an enhanced PCC Vivace protocol, it integrates service-feature awareness, real-time network state feedback, and online adaptive policy generation, enabling low-latency decision-making and scalable deployment. Compared to generic congestion control algorithms, our system achieves a 23% throughput gain, a 37% reduction in end-to-end latency, and a 58% decrease in stalling rate across streaming media, cloud gaming, and vehicle-to-everything (V2X) communication deployments—already validated in production environments. The core contribution lies in shifting the congestion control paradigm from “universal rule design” to “scenario-driven automated customization,” thereby enabling service-aware, adaptive, and deployable congestion control.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent Communication
• Search and Optimization: Algorithm Configuration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Automating congestion control customization for diverse Internet services
• Addressing varying service needs and network conditions challenges
• Adapting PCC Vivace protocol for real-world deployment insights

Innovation

• Automatically customizes congestion control logic
• Leverages PCC Vivace online-learning protocol
• Adapts to diverse service needs and networks