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5G cellular networks are envisioned to support a wide range of emergingdelay-oriented services with different delay requirements (e.g., 20ms forVR/AR, 40ms for cloud gaming, and 100ms for immersive video streaming).However, due to the highly variable and unpredictable nature of 5G accesslinks, existing end-to-end (e2e) congestion control (CC) schemes perform poorlyfor them. In this paper, we demonstrate that properly blendingnon-deterministic exploration techniques with straightforward proactive andreactive measures is sufficient to design a simple yet effective e2e CC schemefor 5G networks that can: (1) achieve high controllable performance, and (2)possess provable properties. To that end, we designed Reminis and throughextensive experiments on emulated and real-world 5G networks, show theperformance 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 lower95th percentile delay while having the same link utilization.

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