Implementing an Experience-driven Low Latency, Low Loss, and Scalable Throughput (L4S) Internet Service Architecture using FreeBSD

Mon, 12 May 2025 00:00:00 +0000

Project Overview

This project explores the integration of Deep Reinforcement Learning (DRL) and Large Language Models (LLMs) with the Low Latency, Low Loss, and Scalable Throughput (L4S) architecture to improve Internet congestion control. Hosted at Deakin University and funded by the APNIC Foundation, the project implements modular congestion control algorithms within FreeBSD, evaluates them on custom-built testbeds, and disseminates experimental results, software and datasets to the wider community.

Key Outcomes

DRL-based Multipath TCP (MPTCP) and AQM modules implemented in FreeBSD
Public testbed infrastructure available for reproducible experimentation
Peer-reviewed publications and ongoing contributions in academic/industry conferences
Student internships, mentoring, and curriculum integration for skill development
Ongoing industry and research partnerships

Open Source Software and Datasets

All source code and supporting data from this project have been made openly available to support reproducibility and collaboration:

Experimental L4S testbed setup: https://github.com/MPTCP-FreeBSD/FreeBSD-L4S-Experiments
DRL-enhanced congestion control stack for FreeBSD: https://github.com/MPTCP-FreeBSD/FreeBSD-DRL-L4S
Predictive congestion marking using LLMs: https://github.com/MPTCP-FreeBSD/L4S-LLM
Adaptive TCP fairness via fine-tuned LLMs: https://github.com/MPTCP-FreeBSD/LLM-TCP
AQM decision models powered by LLM-based inference: https://github.com/MPTCP-FreeBSD/AQM-LLM

Publications

S. R. Pokhrel, J. Kua, D. Satish, S. Ozer, J. Howe, and A. Walid, “DDPG-MPCC: An experience driven multipath performance oriented congestion control,” Future Internet, vol. 16, no. 2, p. 37, Feb. 2024. [Online]. Available: https://doi.org/10.3390/fi16020037
S. K. Shrestha, S. R. Pokhrel, and J. Kua, “On the fairness of internet congestion control over WiFi with deep reinforcement learning,” Future Internet, vol. 16, no. 9, p. 330, Sept. 2024. [Online]. Available: https://doi.org/10.3390/fi16090330
D. Satish, J. Kua, and S. R. Pokhrel, “Active Queue Management in L4S with Asynchronous Advantage Actor-Critic: A FreeBSD Networking Stack Perspective,” Future Internet, vol. 16, no. 8, p. 265, Aug. 2024. [Online]. Available: https://doi.org/10.3390/fi16080265
S. R. Pokhrel, J. Kua, B. Fleming, S. Ozer, J. Howe, and A. Walid, “Multipath TCP implementation under FreeBSD-13 for pluggable machine learning models,” Computer Networks, vol. 252, p. 110671, 2024. [Online]. Available: https://doi.org/10.1016/j.comnet.2024.110671
S. K. Shrestha, S. R. Pokhrel, and J. Kua, “Adapting large language models for improving TCP fairness over WiFi,” arXiv preprint arXiv:2412.18200, Dec. 2024. [Online]. Available: https://arxiv.org/abs/2412.18200
D. Satish, S. R. Pokhrel, J. Kua, and A. Walid, “Distilling large language models for network active queue management,” arXiv preprint arXiv:2501.16734, Jan. 2025. [Online]. Available: https://arxiv.org/abs/2501.16734

Get Involved

Interested in collaborating or learning more? Our project continues to grow through academic and industry partnerships, conference engagement, and educational outreach.