VITA-Group/SymbolicPCC

📜 [NeurIPS 2022] "Symbolic Distillation for Learned TCP Congestion Control", S P Sharan, Wenqing Zheng, Kuo-Feng Hsu, Jiarong Xing, Ang Chen, Zhangyang Wang

37
/ 100
Emerging

This project helps network engineers optimize TCP congestion control. It takes complex, black-box AI policies for managing network traffic and converts them into simple, transparent rules. These rules can then be easily understood, verified, and implemented in network devices, making your network both efficient and predictable. Network operations engineers or performance specialists would find this particularly useful.

No commits in the last 6 months.

Use this if you need to implement high-performing, AI-driven TCP congestion control but require the transparency and simplicity of traditional rule-based systems.

Not ideal if you are looking for an off-the-shelf congestion control solution without the need to understand or modify its underlying logic.

network-performance traffic-management network-operations protocol-optimization telecommunications
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
Community 15 / 25

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Stars

16

Forks

4

Language

C++

License

MIT

Last pushed

Oct 13, 2022

Commits (30d)

0

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