A Simple Non-Deterministic Approach Can Adapt to Complex Unpredictable 5G Cellular Networks

TL;DR

This paper introduces Reminis, a simple non-deterministic congestion control scheme that adapts effectively to the unpredictable and variable nature of 5G networks, improving delay performance while maintaining high link utilization.

Contribution

The paper proposes a novel non-deterministic congestion control approach, Reminis, that is simple, effective, and provably reliable for complex 5G cellular environments.

Findings

Reminis achieves 2.2x lower 95th percentile delay compared to Google BBR2.

Reminis maintains similar link utilization as existing schemes.

Extensive experiments validate the performance benefits of Reminis in real-world 5G networks.

Abstract

5G cellular networks are envisioned to support a wide range of emerging delay-oriented services with different delay requirements (e.g., 20ms for VR/AR, 40ms for cloud gaming, and 100ms for immersive video streaming). However, due to the highly variable and unpredictable nature of 5G access links, existing end-to-end (e2e) congestion control (CC) schemes perform poorly for them. In this paper, we demonstrate that properly blending non-deterministic exploration techniques with straightforward proactive and reactive measures is sufficient to design a simple yet effective e2e CC scheme for 5G networks that can: (1) achieve high controllable performance, and (2) possess provable properties. To that end, we designed Reminis and through extensive experiments on emulated and real-world 5G networks, show the performance benefits of it compared with different CC schemes. For instance, averaged over 60 different 5G cellular links on the Standalone (SA) scenarios, compared with a recent design by Google (BBR2), Reminis can achieve 2.2x lower 95th percentile delay while having the same link utilization.