The article analyzes how AI‑driven elephant flows overload traditional Ethernet networks, causing long‑tail latency and victim‑flow congestion, and explains how adaptive routing, RDMA/ RoCE features, advanced congestion‑control algorithms, and high‑capacity switch chips can mitigate these challenges.
As AI workloads push intelligent compute power growth beyond 50% CAGR, data‑center networks face massive parallel paths, making traditional ECMP load‑balancing insufficient and causing severe congestion, while newer granular schemes such as packet‑spraying, flowlet, and cell‑based balancing offer higher bandwidth utilization and fairness.
This article explains how network congestion affects audio‑video applications and introduces NetEase Cloud Communication’s QoS strategies—including real‑time bandwidth estimation algorithms, adaptive bitrate allocation, and paced sending techniques—to achieve low latency, high stability, and optimal user experience across Wi‑Fi, cellular, and wired networks.
