Adaptive Degradation and Recovery for JD Alliance Recommendation System under High‑Frequency Traffic Spikes
The article presents a comprehensive adaptive degradation and automatic recovery framework for JD Alliance's recommendation system, designed to handle high‑frequency, instantaneous traffic surges during large promotions by combining real‑time monitoring, Wilson‑interval‑based timeout correction, scenario‑aware control, traffic slicing, linear‑programming‑driven chain optimization, and low‑cost business‑agnostic APIs, achieving over 90% reduction in traffic loss and zero incidents.