As AI compute infrastructure evolves into a networked utility, the key challenge shifts from acquiring hardware to organizing distributed resources, data, models, and business needs through intelligent scheduling, governance, and cost-aware orchestration across edge, regional, and national layers.
The article examines how the explosive compute, storage, network, and fault‑tolerance demands of AI large‑model training force a fundamental redesign of system architecture, covering layered storage, optimized All‑Reduce communication, elastic resource orchestration, observability, and cost‑saving strategies.
MaRCA, a multi‑agent reinforcement‑learning framework, allocates compute across JD’s advertising playback chain by jointly estimating user value, resource consumption, and action outcomes while dynamically adjusting to real‑time load, achieving roughly 15 % higher ad revenue without extra compute resources.
Baidu’s Search Content Storage team built an HTAP table storage system and a serverless compute‑scheduling architecture that separates OLTP and OLAP workloads, delivering up to 200 GB/s peak IO, reducing storage cost by 75 %, and enabling SQL‑style task development with native FaaS functions.
iQIYI’s Multi‑AZ unified scheduling architecture combines a unified storage layer (QBFS), an abstracted compute scheduler (QBCS), and a federated metadata service (Waggle Dance) to seamlessly route data and jobs across availability zones, cut storage costs up to 65 %, reduce overall big‑data workload expenses by more than 35 %, and lay the groundwork for future hybrid‑cloud expansion.
This article examines the challenges of uneven compute resource distribution across China and presents Yum China's practical approaches—including multi‑unit deployment, dual‑data‑center scheduling, and supporting platforms—to achieve flexible, cost‑effective compute scheduling for the restaurant sector.
