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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2505.12492 |
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| _version_ | 1866916742567034880 |
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| author | Cohen, Amit Gloukhenki, Lev Hadar, Ravid Itah, Eden Shvut, Yehuda Schapira, Michael |
| author_facet | Cohen, Amit Gloukhenki, Lev Hadar, Ravid Itah, Eden Shvut, Yehuda Schapira, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12492 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unleashing Automated Congestion Control Customization in the Wild Cohen, Amit Gloukhenki, Lev Hadar, Ravid Itah, Eden Shvut, Yehuda Schapira, Michael Networking and Internet Architecture Artificial Intelligence Machine Learning Performance Systems and Control 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. |
| title | Unleashing Automated Congestion Control Customization in the Wild |
| topic | Networking and Internet Architecture Artificial Intelligence Machine Learning Performance Systems and Control |
| url | https://arxiv.org/abs/2505.12492 |