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.

翻译：5G蜂窝网络被设想支持一系列新兴的延迟敏感型服务，这些服务具有不同的延迟需求（例如，VR/AR为20毫秒，云游戏为40毫秒，沉浸式视频流为100毫秒）。然而，由于5G接入链路的高度可变性和不可预测性，现有的端到端（e2e）拥塞控制（CC）方案在这些场景下表现不佳。本文证明，将非确定性探索技术与直接的主动和被动措施合理结合，足以设计一种简单而有效的e2e CC方案，适用于5G网络，该方案能够：（1）实现高可控性能，以及（2）具有可证明的性质。为此，我们设计了Reminis，并通过在仿真和真实5G网络上进行的大量实验，展示了其相比不同CC方案的性能优势。例如，在独立组网（SA）场景下的60个不同5G蜂窝链路上取平均值，与谷歌最近的设计（BBR2）相比，Reminis在保持相同链路利用率的同时，可将第95百分位延迟降低2.2倍。