360 Zhihui Cloud is an enterprise open service platform that aims to "aggregate data value and empower an intelligent future," leveraging 360's extensive product and technology resources to deliver platform services to customers.
This article outlines a step‑by‑step solution for exposing pod domain names inside a corporate network using CoreDNS with an etcd backend, including server and agent deployment, configuration, verification, and practical usage recommendations.
360’s AI platform migrated its GPU clusters to a cloud‑native architecture and adopted the Volcano scheduler, achieving over 45% GPU utilization, less than 7% fragmentation, and more than 1000000 scheduled Pods, while leveraging flexible plugins, hierarchical queues, and resource pooling to optimize AI and big‑data workloads.
This article details the design and implementation of an AI‑driven Intelligent SQL Assistant that automates query parsing, index recommendation, execution‑plan visualization, and supports SQL generation, diagnosis, and explanation across multiple dialects, while outlining its layered architecture, core modules, code examples, and future enhancements.
This article introduces AZ Mover, a lightweight HDFS client‑side tool that intelligently scans, schedules, and migrates block replicas across multiple availability zones, detailing its design goals, core workflow, command‑line options, concurrency controls, and future enhancements for robust big‑data disaster recovery.
This article explains MySQL's memory architecture, why memory usage often stays high after spikes, and provides practical steps—including connection checks, slow query analysis, workload scaling, and switching to jemalloc—to diagnose and reduce memory consumption on 360's internal cloud platform.
This article outlines the background, architectural design, implementation details, and future roadmap of an internet‑boundary cloud firewall built on DPDK, covering physical and software structures, traffic steering, rule matching logic, and planned enhancements such as logging and traffic analysis.
VictoriaMetrics employs a modular, horizontally scalable architecture composed of vmagent, vminsert, vmstorage, vmselect, and vmalert, each handling data collection, ingestion, storage, querying, and alerting, while leveraging consistent hashing, LSM‑tree storage, TSID indexing, and multi‑tenant isolation to efficiently manage large‑scale time‑series workloads.
This article presents a comprehensive benchmark of DeepGEMM, Cutlass, and Triton on NVIDIA H20 and H800 GPUs, analyzing TFLOPS, bandwidth, latency, and speedup across various matrix sizes, and concludes which library is optimal for different workload scenarios.
This article explains Application Performance Monitoring (APM), its key benefits such as business continuity, performance optimization, and cost reduction, outlines essential APM modules, and details Yunzhou Observation’s OpenTelemetry‑based design, data ingestion, processing, visualization, and future roadmap for observability.
This article details 360 Cloud Platform's practical implementation of GPU passthrough and Docker‑MIG solutions, covering the underlying principles, host and OpenStack configuration steps, verification methods, and future directions for full GPU virtualization.
