🤖 AI Summary
This study addresses bufferbloat issues in ultra-low-latency applications such as cloud gaming and augmented reality, which are exacerbated by traditional congestion control mechanisms. To enable accurate evaluation of Low Latency, Low Loss, Scalable throughput (L4S) architectures, the authors present the first complete implementation of an L4S-compliant end-host protocol stack in ns-3. The core contribution involves porting the TCP Prague algorithm from the Linux kernel v6.12 into ns-3 and integrating Accurate ECN (AccECN) feedback. A key technical challenge—reconciling kernel-level, byte-granular window and rate control logic with ns-3’s packet-level simulation model—was successfully resolved. Experimental validation demonstrates that the proposed model faithfully reproduces real-world congestion response behaviors observed in physical testbeds, thereby providing a reproducible and high-fidelity simulation platform for L4S performance assessment.
📝 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.