A DualPI2 Module for Mahimahi: Behavioral Characterization and Cross-Platform Analysis

TL;DR

This paper introduces a user-space DualPI2 module for Mahimahi, enabling reproducible L4S experiments and analyzing behavioral differences across platforms to improve cross-environment alignment.

Contribution

It presents a modular, user-space DualPI2 implementation for Mahimahi and provides empirical analysis of cross-platform behavioral discrepancies and parameter sensitivities.

Findings

Behavioral differences exist between Mahimahi and Linux implementations.

Parameter sensitivity varies with network conditions, affecting alignment.

Targeted parameter tuning improves cross-platform consistency in low BDP regimes.

Abstract

Low Latency, Low Loss, and Scalable Throughput (L4S) is an emerging paradigm for latency control based on DualPI2 active queue management and scalable congestion control. While a Linux kernel implementation of DualPI2 is available, controlled and reproducible experimentation on L4S mechanisms can be facilitated by a modular, user-space alternative. In this paper, we present a DualPI2 module for the Mahimahi network emulator, designed to support extensible, component-level experimentation without kernel modification. We conduct a statistical behavioral characterization of the Mahimahi implementation by examining key metrics across diverse traffic patterns and network conditions, using the Linux kernel implementation as a reference baseline. Our analysis shows that behavioral alignment across execution environments is not automatic: identical DualPI2 parameterization does not guarantee identical dynamics. Instead, key control parameters exhibit environment-dependent sensitivity, leading to regime-dependent discrepancies across bandwidth-delay product (BDP) conditions. Through targeted parameter exploration, we identify configurations that improve cross-platform alignment in low BDP regimes, while revealing structural differences that persist under higher load. This work provides both a practical tool for experimental L4S research and empirical insight into cross-platform behavioral differences, highlighting the importance of systematic characterization and environment-aware parameter selection in emulation-based AQM studies.