ElasticJob is a Java‑based distributed scheduling framework that blends Quartz‑style job timing with ZooKeeper coordination, offering lightweight Lite and cloud‑native Cloud editions, elastic scaling, resource governance, and a roadmap toward Kubernetes support and plug‑in extensibility.
This article analyzes Docker's declining market share and provides a detailed comparison of alternative container runtimes—including rkt, Mesos, LXC, OpenVZ, and Containerd—highlighting their features, advantages, and limitations for various production scenarios.
This article explains the Kubernetes architecture, details each core component, demonstrates how a Deployment is created, and critically compares Kubernetes' two‑layer scheduling model with Mesos, evaluating resource utilization, scalability, flexibility, performance, and scheduling latency while discussing why cluster schedulers struggle with horizontal scaling.
This article describes how we identified underutilized CPU and memory resources in our company's servers, evaluated Kubernetes versus Apache Mesos, and built a non‑intrusive, Mesos‑based multi‑task scheduling system with dynamic resource reservation, monitoring, task isolation, and cluster‑wide observability, while addressing deployment challenges.
This article explains how we leveraged Apache Mesos to create a multi‑task scheduling system that maximizes idle CPU and memory on legacy CentOS machines without kernel upgrades, detailing architecture, deployment, monitoring, resource isolation, and remaining challenges.
The talk details XtraPi’s journey from early PBS‑based supercomputers to a modern Kubernetes‑driven, multi‑cloud platform that uses Tencent Cloud TKE to run massive containerized drug‑discovery simulations, describing scaling strategies, image optimization, CI pipelines, checkpoint‑restart, and future serverless and bare‑metal enhancements.
The article describes how the Nitro team designed and implemented a Go‑based consistent‑hashing router that leverages Nginx, the Redis protocol, Envoy sidecars, and Mesos to provide low‑latency, high‑throughput request routing and caching for compute‑intensive services.
This article details how 360's commercial advertising platform leverages Mesos and Docker containerization to solve data‑center migration, fault recovery, OS inconsistencies, and resource‑utilization challenges, describing the architecture, standardization, networking, storage, service discovery, and future plans.
An SRE engineer from 360 shares how Mesos and Docker containerization solved data‑center migration, fault recovery, OS inconsistencies, scaling, and resource inefficiencies in the company's advertising platform, detailing architecture, deployment, networking, storage, service discovery, and future plans for cloud‑native operations.
This article surveys the architecture of cluster resource managers and task schedulers—covering definitions, design principles, and three main categories (centralized, two‑level, and shared‑state) with concrete examples such as Hadoop YARN, Mesos, Spark Drizzle, Borg and Kubernetes—while highlighting their trade‑offs in scalability, fault‑tolerance, and flexibility.
Learn how to construct a scalable data processing platform using the SMACK stack—Spark, Mesos, Akka, Cassandra, and Kafka—covering storage design, processing workflows, resource management, deployment options, and fault‑tolerant task execution for both batch and streaming workloads.
This article describes how the OPS team designed and implemented a private‑cloud Elasticsearch service using Mesos for resource management, Docker containers orchestrated by Marathon, and a suite of monitoring, self‑service configuration, and continuous deployment tools to improve resource utilization and operational efficiency.
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.
This article analyzes the technical and organizational factors that determine when Docker containers are appropriate versus virtual machines, covering scenarios from small startups to large enterprises, and presents ten deployment modes ranging from public‑cloud VMs to advanced Kubernetes and hybrid solutions.
At iQIYI, the author led a private‑cloud platform built on Mesos and Chronos that runs millions of weekly containers for transcoding and analytics, then layered a feature‑rich PaaS called QAE on Marathon, highlighting IaaS vs PaaS benefits and outlining future micro‑service, GPU, and scaling enhancements.
Juice is an open‑source Mesos‑based distributed task scheduling framework that abstracts Docker tasks, provides REST APIs, leverages Redis queues, and implements custom resource allocation, offering high resource utilization and fault‑tolerant execution for audio‑video processing workloads.
This article describes how a team migrated Spark 1.6.x running on Mesos to a Marathon‑Docker based architecture that provides dynamic executor scaling, resolves configuration and resource‑allocation issues, and improves monitoring, fault‑tolerance, and upgrade processes for large‑scale streaming workloads.
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 details Ctrip’s practical investigation of Windows containers, explaining why they adopted them for .NET workloads, the migration process, container types, image handling, storage and network models, orchestration choices such as Docker, Mesos, and the remaining challenges and future plans.
The article describes how Hujiang built and iterated a three‑generation asynchronous task scheduling platform—Juice—using Mesos and Docker to achieve on‑demand resource allocation, horizontal scalability, and efficient handling of large multimedia processing workloads.
This article details Dangdang’s experience building a private job‑cloud platform using Mesos and a custom framework, covering technology selection, key features, integration challenges with Elastic‑Job, resource scheduling, and practical recommendations for Mesos‑based Java workloads.
This article explains the fundamentals of consensus in distributed systems, compares Paxos and Raft, and provides a step‑by‑step guide with code snippets on embedding the etcd/raft library into the open‑source Mesos scheduler Swan to achieve reliable multi‑node data synchronization.
This article explains the Mesos distributed system kernel, its resource‑allocation workflow, and how Qunar engineers applied and evolved Mesos, Marathon, and custom frameworks to achieve fine‑grained scheduling, high availability, service discovery, and multi‑tenant management in a large‑scale production environment.
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.
The article examines how CaaS providers are replacing Docker with RunC‑based solutions such as CRI‑O, Unified Containerizer, and Garden, outlines the evolution of the container ecosystem, discusses the strategic implications for production environments, and forecasts future trends and community contributions.
This article describes how Qunar's operations team designed and implemented a cloud‑native Elasticsearch‑as‑a‑Service platform using Mesos, Marathon, and Docker, covering requirements analysis, technology selection, resource quota management, cluster isolation, service discovery, data reliability, monitoring, automated deployment, and future improvements.
This article details China Mobile Zhejiang's journey from early virtualization to a full private‑cloud platform built on MESOS, covering why MESOS was chosen, the evolution of their cloud stages, DCOS implementation, automatic scaling, service discovery, and the operational benefits achieved.
This tutorial introduces Apache Mesos, Zookeeper, and Marathon, explains their core components and coordination mechanisms, and provides detailed, image‑rich step‑by‑step instructions for setting up a pseudo‑cluster, deploying Docker containers, and managing tasks through the Mesos and Marathon web interfaces.
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 describes how Qunar's DevOps team built a Mesos‑powered log analysis platform using ELK, Marathon, Chronos, and custom monitoring frameworks, detailing deployment architecture, resource management, container logging, image handling, and operational lessons learned.
