Implementing the L4S Architecture in the ns-3 Simulator
2026-03-20 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors address the problem of delays in internet connections caused by old congestion control methods, which is important for apps like cloud gaming and augmented reality. They implemented a new system called L4S, which helps reduce delays and improve performance, into a network simulator called ns-3. By adapting parts of the Linux kernel's TCP Prague protocol and adding accurate feedback signals, they created a model that closely mimics real-world behavior. Their work offers researchers a reliable tool to test and study low-latency network techniques in a controlled setting.
congestion controlbufferbloatL4STCP PragueAccECNns-3 network simulatorlow-latency queue managementLinux kernelnetwork simulationcloud gaming
Authors
Maria Eduarda Veras, Eduardo Freitas, Assis T. de Oliveira Filho, Djamel Sadok, Judith Kelner
Abstract
The demand for ultra-low latency in modern applications, such as cloud gaming and augmented reality, has exposed the limitations of traditional congestion control algorithms regarding bufferbloat. The Low Latency, Low Loss, and Scalable Throughput (L4S) architecture addresses this challenge by combining scalable congestion controls, such as TCP Prague, low-latency queue management with prioritization, and Accurate ECN (AccECN) feedback. Although Linux kernel implementations exist, the research community lacks a complete, high-fidelity model within the Network Simulator 3 (ns-3) for reproducible experiments. This paper presents an implementation of end-host protocols for the L4S architecture in ns-3, focusing on the porting of TCP Prague from the Linux kernel (v6.12) and the integration of AccECN signaling. Significant engineering challenges regarding the adaptation of kernel logic are detailed, particularly the reconciliation of Linux's packet-based arithmetic with ns-3's byte-based architecture for window management and pacing. Simulation results demonstrate that the proposed model faithfully reproduces the congestion response behaviors observed in real-world testbed scenarios, validating the platform's accuracy. Consequently, this work provides the community with a validated toolset for complex L4S performance evaluations in controlled environments.