In a keynote at CCF HPC China 2024, Alibaba Cloud’s VP explains how diversified high‑performance computing workloads, elastic cloud resources, and the proprietary CIPU architecture are driving the shift to a data‑plus‑AI era across industries such as automotive, life‑science, and large‑model training.
The talk outlines Baidu Smart Cloud's comprehensive cloud‑native redesign—including ultra‑elastic compute, AI‑focused storage, high‑performance networking, AI‑driven operations, and edge‑distributed services—illustrated with automotive and fintech case studies that demonstrate how enterprises can accelerate digital transformation in the AI‑native age.
This article presents OPPO’s next‑generation big‑data and AI integrated architecture on functional cloud, detailing a cloud‑native elastic compute framework, a unified data‑lake solution, real‑time feature platforms, machine‑learning data acceleration, and hybrid‑cloud deployments, highlighting performance gains and cost reductions.
This article analyzes the rising compute demand in modern deep‑learning‑driven search systems, proposes a micro‑ and macro‑level adaptive power‑allocation framework, models the optimization problem with cost, time, and feasibility constraints, and details an elastic cascading architecture that dynamically balances resource usage, system state, and traffic value to achieve higher ROI and stability.
Facing rapid growth in offline data and compute demands, we migrated our big‑data platform to a cloud‑native architecture using Spark 3.2.3 on Kubernetes with OSS‑HDFS storage, achieving elastic scaling, cost reduction, and compute‑storage separation while detailing implementation, challenges, and operational insights.
This article details Huya's SRE-driven architecture that combines center‑edge deployment, high containerization, AI‑powered elasticity, fault avoidance, and fast recovery mechanisms to achieve deterministic, highly available live‑streaming services.
The article analyzes the challenges of moving traditional on‑premise big‑data platforms to the cloud, outlines the cost‑saving opportunities of cloud‑native data lakes, presents three core architectural principles, and reviews Tencent Cloud's data lake product suite and its key use cases.
This talk presents Alibaba's intelligent marketing platform, detailing its three ad systems, the massive data and low‑latency requirements, and the architectural solutions—including cold‑hot data separation, OLDB storage, full‑link tracing, elastic compute, and platform‑level componentization—that achieve high performance, high availability, and cost‑effective operation.
The article details JD.com’s 618 shopping festival engineering, describing how componentized micro‑services, a unified data platform, and the Archimedes elastic scheduling system enabled billions of requests, real‑time data processing and seamless online‑offline integration without adding new server resources.
This article describes Tencent’s elastic computing platform built to harness idle on‑premise resources for massive image, video, AI, and log processing workloads, detailing the architectural layers, strategies for protecting online service capacity, latency, scheduling and fault rates, and the practical lessons learned from its deployment.
This article describes the design, challenges, and operational practices of a cloud‑native elastic computing platform that reuses idle resources from production servers to support massive image compression, video transcoding, AI inference, and log processing while ensuring online services maintain performance, latency, and reliability.
The article describes how an elastic container‑based computing platform replaces tens of thousands of physical servers to deliver billions of daily image‑compression operations, while also supporting video transcoding, Spark jobs, and AI workloads through resource isolation, named services, dynamic scheduling, and load‑balancing techniques.
Tencent’s elastic computing platform replaces 24,000 physical servers with just 6,000 containers, delivering sustainable compute for billions of daily image compressions while also supporting video transcoding, Spark jobs, and AI workloads through dynamic resource isolation, named services, and intelligent scheduling.
