Netflix’s cloud‑gateway team redesigned its load‑balancing stack—combining client latency, server utilization, and probabilistic choice‑of‑2 algorithms—to dramatically lower error rates, improve request distribution, and enhance fault‑tolerance for millions of requests per second.
This article explains the growing need for speech dereverberation in audio‑video conferencing, outlines the physical causes of reverberation, reviews historical research, and details NetEase Cloud's adaptive dual‑mic signal‑correlation approach, algorithm implementations, performance optimizations, and future directions.
This article reviews the challenges of compressing massive time‑series data, surveys existing methods, and introduces a novel two‑stage deep reinforcement learning framework (AMMMO) that adaptively selects compression modes, demonstrating significant compression ratio improvements and high throughput on large‑scale IoT and server workloads.
