JD.com's Archimedes is a data center operating system that provides one-click scaling, intelligent scheduling, mixed deployment, and multi-site active-active capabilities, significantly improving resource utilization and reducing IT costs.
The article explains how Kubernetes has won the container‑orchestration battle, is evolving into a universal low‑level layer for multi‑cloud deployments, and is reshaping software standards, package management, serverless workloads, and the economics of distributed systems.
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.
This article explains how a Docker‑based container cloud platform solves resource waste, isolation, and deployment challenges for startups by standardizing images, using Rancher for orchestration, implementing CI/CD pipelines, and managing logs and service registration to achieve seamless 24/7 continuous delivery.
UCloud’s Compute Factory enables rapid provisioning of massive VM resources for compute‑intensive services by leveraging a Mesos‑based resource management platform that unifies multi‑region data centers, supports both VMs and containers, and addresses challenges in scheduling, networking, storage, and operational reliability.
Since September 2016, JD’s AI foundation platform has leveraged Docker and Kubernetes to build a scalable machine‑learning infrastructure that now runs over 5,000 container instances, supports more than 20 AI services, and provides unified GPU, storage, networking, and multi‑tenant capabilities for both inference and training workloads.
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.
This article explains how to build a DevOps workflow using container orchestration, covering agile development with Docker image slimming, a Jenkins‑based CI/CD pipeline, simplified Gitflow, container monitoring with cAdvisor‑InfluxDB‑Grafana, and auto‑scaling via Marathon, while separating automated deployment from production release.
This article details JD.com's Elastic Cloud 1.0 platform—its massive container deployment, four‑principle philosophy, architectural design, operational challenges, performance optimizations, and the roadmap toward Elastic Cloud 2.0—offering practical insights for large‑scale cloud engineering.
The article details Hujiang's practical experience deploying microservices with Docker, evaluating orchestration tools, network and storage solutions, monitoring approaches, and automatic scaling, and demonstrates how Docker improves deployment density, QPS, and resource efficiency compared to traditional architectures.
This article details Ctrip's journey in building a private container cloud to support rapid business growth, covering elasticity challenges, container deployment principles, orchestration platform choices, network design, operational issues, custom executors, monitoring solutions, and the overarching CDOS system.
This article explains the architecture of Docker Swarm, the reasons for running it on Apache Mesos, the integration process—including resource offers, task scheduling, and container creation—and discusses current limitations and future improvement directions.
Since the end of 2016, JD has been transitioning its infrastructure from OpenStack to Kubernetes, completing 20% of the migration and aiming for full conversion by Q2, and shares detailed experiences, architectural evolution, operational practices, and future directions for large‑scale container platforms.
In this architects' roundtable, leading engineers from companies such as Hujiang, Didi, Mogujie, Qiniu, and Shanbay share practical experiences and challenges of adopting Docker, Mesos, Marathon, and Kubernetes for container deployment, orchestration, networking, storage, and auto‑scaling in production environments.
This article introduces Docker 1.12’s Swarm mode, detailing its new features such as service creation, health checks, scaling, scheduling constraints, load balancing, service discovery, and rolling updates, and provides step‑by‑step instructions for building a multi‑node Swarm cluster, deploying services, and handling node failures.
This guide walks you through setting up a Docker Swarm cluster on three Ubuntu 16.04 servers, covering environment preparation, Docker installation, configuring a Consul discovery backend, creating manager and worker nodes, and managing containers across the swarm using the Docker remote API.
This guide explains Docker Swarm’s architecture, installation methods, and step‑by‑step procedures for creating a high‑availability Swarm cluster, including manager and node setup, TLS security, service discovery with Consul, and essential Docker commands for operating and testing the cluster.
This article summarizes a 2016 Global Operations conference talk that explains how Docker and micro‑services reshape software delivery, covering Alibaba's container usage, cloud‑native principles, continuous integration, immutable infrastructure, and practical tips for deploying containers at scale.
This article details how Ping An Technology designed and operated the Padis platform—an internal Docker‑based PaaS that evolved to Mesos + Marathon, providing independent container IPs, automated networking, load balancing, CMDB integration, monitoring, and log aggregation to support thousands of containers in production financial applications.
This article details a year‑long journey of designing, deploying, and operating container‑based development environments using Docker, Apache Mesos, and Kubernetes, covering the challenges of version consistency, rapid environment switching, resource isolation, and the practical solutions and lessons gathered from real‑world production use.
This article introduces Kubernetes, Google's open‑source container orchestration platform, explaining its core concepts such as Pods, ReplicationControllers, Services, and Labels, and detailing the main components like kubectl, kube‑apiserver, controller‑manager, scheduler, proxy, and kubelet, along with their OpenStack equivalents.
This article introduces Kubernetes, explains why it is used, outlines its core goals, describes the main components and their functions, discusses the architectural improvements it enables, and shares practical deployment experiences and common issues encountered during real‑world usage.
This article introduces Kubernetes fundamentals, explains core concepts such as containers, clusters, pods, services, and labels, and provides a detailed, step‑by‑step guide for setting up a simple Kubernetes cluster on Ubuntu using VirtualBox and Vagrant.
Project Eru, a homegrown Docker‑based orchestration system developed at Mango TV, replaces earlier PaaS attempts with a stateless, scalable core and agent architecture, leveraging Redis clusters, MacVLAN networking, and fine‑grained CPU allocation to achieve rapid, automated scaling across thousands of containers.
The article recounts the evolution from Douban's App Engine to Mango TV's Nebulium Engine and finally Project Eru, describing how Docker, Redis Cluster, MacVLAN networking, and custom resource scheduling were combined to build a scalable, cloud‑native platform for heterogeneous workloads.
Docker, an open-source tool written in Go, packages applications into lightweight Linux containers that share the host kernel, offering near-native performance, a client-server architecture with images and containers, simple bridge networking, essential CLI commands, and an evolving ecosystem including CoreOS, Kubernetes, and PaaS platforms.
