{"id":38259,"date":"2026-06-05T10:20:37","date_gmt":"2026-06-05T14:20:37","guid":{"rendered":"https:\/\/www.lpi.org\/articles\/\/"},"modified":"2026-06-05T10:21:16","modified_gmt":"2026-06-05T14:21:16","slug":"how-kubernetes-came-to-dominate-large-scale-computing-part-2","status":"publish","type":"post","link":"https:\/\/www.lpi.org\/fr\/blog\/2026\/06\/05\/how-kubernetes-came-to-dominate-large-scale-computing-part-2\/","title":{"rendered":"How Kubernetes Came to Dominate Large-Scale Computing: Part 2"},"content":{"rendered":"

The first article<\/a> in this two-part series laid out the landscape of distributed computing just before containers became a viable option. Now we\u2019ll bring the story up to date.<\/p>\n

Although the term \u00ab\u00a0containers\u00a0\u00bb originated with the Docker project, the idea is usually traced back to a feature that the FreeBSD operating system released in 2000, creatively termed \u00ab\u00a0jails<\/a>.\u00a0\u00bb That feature in turn was based on a much older Unix concept called \u00ab\u00a0chroot,\u00a0\u00bb which isolated a process by defining a particular directory as its root directory and barring access to any part of the filesystem outside that directory. Jails did not offer enough features to be widely used, and no other operating system implemented them.<\/p>\n

In early 2008, the Linux kernel incorporated cgroups<\/a> (control groups), which had been developed at Google in 2006. This feature allows administrators at the kernel level to isolate processes, limit their memory and CPU usage, and do other process management. Numerous virtualization tools, including QEMU and XEN, started using cgroups. Virtualization crept along.<\/p>\n

The year 2013 was the critical turning point, witnessing the development of both Docker and Kubernetes. March 2013 brought the public announcement of Docker<\/a>. Google had been talking about a container orchestration platform in the Summer of 2013<\/a>, and released Kubernetes in July 2015.<\/p>\n

Docker was a creature of our time. It runs on Linux and uses commands in a Linux-like way with which developers and administrators are comfortable.<\/p>\n

Like other modern administrative tools, Docker consults a configuration file to create images. It has a bit of complexity; for instance, each build runs in two stages, the first including all the tools required to compile an instance and the second containing just the minimal environment needed during a run. But the process is still fairly simple, and allows quick rebuilds for the continuous updates that modern software calls for.<\/p>\n

With Docker (and then Kubernetes), the sweet spot of distributed computing was filled. But there had to be solutions for the many administrative tasks the distributed computing is based on. The developers of Kubernetes didn’t solve all the problems\u2014they weren’t about to invent another CORBA, DCE, or OpenStack. But they set the stage for solutions that were quick to come. Let\u2019s look at the tools that were needed.<\/p>\n

Tasks of Orchestration<\/h3>\n

\u00ab\u00a0Orchestration\u00a0\u00bb is the abstract and perhaps daunting term routinely used as a catch-all for the kind of administrative tasks that might also be collected under the abstract and daunting term \u00ab\u00a0governance.\u00a0\u00bb Specifically, some of the crucial tasks that developers and administrators need to perform in distributed computing are:<\/p>\n

Automatic scaling<\/h4>\n

Many applications have rapidly changing loads, usually because web visitors send a lot of requests at once (a phenomenon noted many decades ago as the SlashDot effect<\/a>). A distributed computing system should note the increase in traffic, add new nodes in the cloud as needed, and quickly release them when no longer needed.<\/p>\n

Automatic restart and maintaining state<\/h4>\n

If you’ve determined that you need 30 nodes to handle your load, you should replace one when it fails. Hopefully, an administrator will be alerted to the failure (whether caused by a software bug, network disconnection, or hardware failure) and fix the underlying cause. But in the meantime, the distributed system should automatically fire up an identical instance of the failed node to maintain the desired state of the system.<\/p>\n

Load balancing<\/h4>\n

Usually a separate proxy is used to distribute requests in a fair manner among worker nodes.<\/p>\n

Service discovery<\/h4>\n

Whether through a dedicated DNS server or through some other mechanism, nodes should be able to find the services on which they depend.<\/p>\n

Rolling updates<\/h4>\n

This topic touches on another key factor in modern computing (outside the topic of this series): frequent, incremental software updates. You want existing requests from users to be handled while you update each node, so the distributed system should selectively take down inactive nodes and replace them with new ones until every node is up to date.<\/p>\n

These tasks also require logging, monitoring, and alerting, along with a number of other things it would be nice to automate.<\/p>\n

The Juggernaut of Kubernetes<\/h2>\n

In November 2014, one and a half years after the first release of Docker, version 1.0 of a service called Swarm, offering all the features on the previous list, was released<\/a>. Although popular, it has not dampened the industry’s enthusiasm for Kubernetes and is generally seen in testing or for small projects.<\/p>\n

Nowadays, Docker is still seen as a critical tool for building images of containers, but they usually run on Kubernetes.<\/p>\n

Red Hat created an almost drop-in replacement for Docker<\/a> in 2018 called Podman. The main appeal of Podman is that it runs in user mode, without requiring root privilege on the host system. (Docker can also run in \u00ab\u00a0rootless mode\u00a0\u00bb, but with reduced features and performance<\/a>.) Podman does not work with Swarm because of architectural differences with Docker, and there will never be a \u00ab\u00a0Podman Swarm.\u00a0\u00bb For scaling and other orchestration tasks, Podman developers rely on Kubernetes.<\/p>\n

(Red Hat has thrown its strategy fully into Kubernetes. In addition to developing Podman and numerous other tools as part of the Kubernetes universe, it based its major product, the OpenShift cloud<\/a>, on Kubernetes.)<\/p>\n

An example of the power brought by Kubernetes is its Persistent Volume features. Their purpose is as follows: containers (as well as virtual machines and FaaS instances) are ephemeral and designed so they can disappear silently without an effect on the overall service. That means that any state that has to be preserved (such as retail customer information) and any data that has to be used by a future program has to be stored outside the containers. Persistent Volumes fill that need fairly elegantly, and are tightly bound into Kubernetes.<\/p>\n

I think that the success of Kubernetes can be attributed to vigorous efforts by Google, a company with enormous resources, along with good timing.<\/p>\n

Google based Kubernetes on internal projects<\/a>, investing so much in it that management resisted making it open source. The key consideration that won them over appears in the article just cited: \u00ab\u00a0customers were paying for a lot of CPUs, but their utilization rates were extremely low because they were running VMs.\u00a0\u00bb<\/p>\n

In other words, Google calculated that by releasing a powerful orchestration tool that customers could trust\u2014both because it was backed by Google and, at the same time, was free and open source\u2014they would win much more business for their cloud. And indeed, every PaaS vendor, such as Cloud Foundry<\/a> (an open source project created by VMware in 2009<\/a>), has welcomed Kubernetes as the tool for running containers on their service.<\/p>\n

As with Android, the industry expects a major product by Google to be backed up with support. At the same time, open source builds trust because no one can take away the software or force its development into paths that run contrary to user needs. Unlike many companies, though, who throw software over the wall and call it open source, Google didn’t stop with this release. Within a year\u2014in July 2015\u2014Google partnered with another leviathan<\/a> of open source computing, the Linux Foundation, to start the vendor-neutral Cloud Native Computing Foundation (CNCF)<\/a>.<\/p>\n

CNCF has signed up virtually all the heavyweights in computing and has served as incubator for many administrative tools. The word \u00ab\u00a0cloud\u00a0\u00bb here is used in a limited way. They’re not talking about virtual machines. Almost everything they do is directed toward Kubernetes.<\/p>\n

And even without Red Hat and CNCF pouring resources into Kubernetes, the community would create all the tools that ease the pain of Kubernetes’s notoriously complex management. If you hear of a software utility whose name begins with a K, you can bet that it works specifically with Kubernetes. (The free desktop KDE uses the same convention, but it serves such a different set of use cases that the two sets of K’s are not likely to confuse anyone.)<\/p>\n

In conclusion, among a welter of competing projects, both proprietary and open source, Kubernetes won by coming at just the right time, responding to the real-life needs of customers, and benefiting from backing by powerful institutions. Its open source licensing and open source community involvement are critical factors that make it one of the most important projects in modern computing.<\/p>\n

<< Read the first part of this article<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

The first article in this two-part series laid out the landscape of distributed computing just before containers became a viable option. Now we\u2019ll bring the story up to date. Although the term \u00ab\u00a0containers\u00a0\u00bb originated with the Docker project, the idea … En savoir plus<\/a><\/p>\n","protected":false},"author":44,"featured_media":38260,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1],"country":[],"language":[304],"ppma_author":[495],"class_list":["post-38259","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-none","language-english"],"acf":[],"yoast_head":"\nHow Kubernetes Came to Dominate Large-Scale Computing: Part 2 - Linux Professional Institute (LPI)<\/title>\n<meta name=\"description\" content=\"The second article explores how Docker, CNCF, and open source helped Kubernetes dominate large-scale computing.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.lpi.org\/fr\/blog\/2026\/06\/05\/how-kubernetes-came-to-dominate-large-scale-computing-part-2\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How Kubernetes Came to Dominate Large-Scale Computing: Part 2\" \/>\n<meta property=\"og:description\" content=\"The second article explores how Docker, CNCF, and open source helped Kubernetes dominate large-scale computing.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.lpi.org\/fr\/blog\/2026\/06\/05\/how-kubernetes-came-to-dominate-large-scale-computing-part-2\/\" \/>\n<meta property=\"og:site_name\" content=\"Linux Professional Institute (LPI)\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/LPIConnect\" \/>\n<meta property=\"article:published_time\" content=\"2026-06-05T14:20:37+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-06-05T14:21:16+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.lpi.org\/wp-content\/uploads\/2026\/06\/article-Kubernetes-02-02.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1440\" \/>\n\t<meta property=\"og:image:height\" content=\"994\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Andrew Oram\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@lpiconnect\" \/>\n<meta name=\"twitter:site\" content=\"@lpiconnect\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Andrew Oram\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.lpi.org\\\/fr\\\/blog\\\/2026\\\/06\\\/05\\\/how-kubernetes-came-to-dominate-large-scale-computing-part-2\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.lpi.org\\\/fr\\\/blog\\\/2026\\\/06\\\/05\\\/how-kubernetes-came-to-dominate-large-scale-computing-part-2\\\/\"},\"author\":{\"name\":\"Andrew Oram\",\"@id\":\"https:\\\/\\\/www.lpi.org\\\/fr\\\/#\\\/schema\\\/person\\\/3406dc82718aebf8c9e647fe3a54116a\"},\"headline\":\"How Kubernetes Came to Dominate Large-Scale Computing: Part 2\",\"datePublished\":\"2026-06-05T14:20:37+00:00\",\"dateModified\":\"2026-06-05T14:21:16+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.lpi.org\\\/fr\\\/blog\\\/2026\\\/06\\\/05\\\/how-kubernetes-came-to-dominate-large-scale-computing-part-2\\\/\"},\"wordCount\":1392,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/www.lpi.org\\\/fr\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.lpi.org\\\/fr\\\/blog\\\/2026\\\/06\\\/05\\\/how-kubernetes-came-to-dominate-large-scale-computing-part-2\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.lpi.org\\\/wp-content\\\/uploads\\\/2026\\\/06\\\/article-Kubernetes-02-02.jpg\",\"articleSection\":[\"- None -\"],\"inLanguage\":\"fr-FR\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/www.lpi.org\\\/fr\\\/blog\\\/2026\\\/06\\\/05\\\/how-kubernetes-came-to-dominate-large-scale-computing-part-2\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.lpi.org\\\/fr\\\/blog\\\/2026\\\/06\\\/05\\\/how-kubernetes-came-to-dominate-large-scale-computing-part-2\\\/\",\"url\":\"https:\\\/\\\/www.lpi.org\\\/fr\\\/blog\\\/2026\\\/06\\\/05\\\/how-kubernetes-came-to-dominate-large-scale-computing-part-2\\\/\",\"name\":\"How Kubernetes Came to Dominate Large-Scale Computing: Part 2 - 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