• Building Images with Dockerfile and Docker Hub

  In this series previewing the self-paced Containers for Developers and Quality Assurance (LFS254) training course from The Linux Foundation, we’ve covered installing Docker, introduced Docker Machine, and some basic commands for performing Docker container and image operations. In the three sample videos below, we’ll take a look at Dockerfiles and Docker Hub.

  Docker can build an image by reading the build instructions from a file that’s generally referred to as Dockerfile. So, first, check your connectivity with the “dockerhost” and then create a folder called nginx. In that folder, we have created a file called dockerfile and in the dockerfile, we have used different instructions, like FROM, RUN, EXPOSE, and CMD.

• What can developers learn from being on call?

  We often talk about being on call as being a bad thing. For example, the night before I wrote this my phone woke me up in the middle of the night because something went wrong on a computer. That’s no fun! I was grumpy.

  In this post, though, we’re going to talk about what you can learn from being on call and how it can make you a better software engineer!. And to learn from being on call you don’t necessarily need to get woken up in the middle of the night. By “being on call”, here, I mean “being responsible for your code when it breaks”. It could mean waking up to issues that happened overnight and needing to fix them during your workday!

• Making the Internet Archive’s full text search faster.

  The Internet Archive is a nonprofit digital library based in San Francisco. It provides free public access to collections of digitized materials, including websites, books, documents, papers, newspapers, music, video and software.

  This article describes how we made the full-text organic search faster — without scaling horizontally — allowing our users to search in just a few seconds across our collection of 35 million documents containing books, magazine, newspapers, scientific papers, patents and much more.

• DevOps: More Than Automation

  Type “devops” into any job search site today and the overwhelming majority of results will be for some variation of “DevOps Engineer”. The skills required will centre on tools like Puppet/Chef/Ansible, AWS/Azure, scripting in Python/Perl/Bash/PowerShell etc. Essentially, they’ve taken a deployment automation engineer role, crossed out “deployment automation” and written “DevOps” in its place.

The US is falling behind in the supercomputer race, but no matter where a supercomputer is running, one thing remains true: It's running Linux.

In the latest Top500 Supercomputer competition, which was revealed in June 2017, 498 out of 500 supercomputers were running Linux. Of the remaining two, both ran Unix.

These were a pair of Chinese IBM POWER computers running AIX near the bottom of the list. These machines came in at 493 and 494. Since the November 2016 Top500, these supercomputers have dropped by over 100 places. At this rate, Linux will score a clean sweep in the next biannual Top500 competition.

When the first Top500 supercomputer list was compiled in June 1993, Linux was barely more than a toy and hadn't adopted Tux as its mascot yet. Starting in 1998, when it first appeared on the Top500, Linux quickly dominated supercomputing.

• Cloud Native Apps and Security: The Case for CoreOS Rkt and Xen

  CoreOS's rkt started at the beginning of 2014 as a security-focused alternative to Docker. The project aimed to create a signature verification of cloud-native apps by default; the intention was to guarantee the integrity of the apps. It also stepped away from the central-daemon design of Docker, which requires root privileges for all operations. By contrast, the rkt process is short-lived, limiting the chances of being exploited, and some of rkt commands can be executed as unprivileged user.

• U.S. Slips in New Top500 Supercomputer Ranking

  Tapwrit was the second favorite at Belmont, and Sunway TaihuLight was the clear pick for the number-one position on TOP500 list, it having enjoyed that first-place ranking since June of 2016 when it beat out another Chinese supercomputer, Tianhe-2. The TaihuLight, capable of some 93 petaflops in this year’s benchmark tests, was designed by the National Research Center of Parallel Computer Engineering & Technology (NRCPC) and is located at the National Supercomputing Center in Wuxi, China. Tianhe-2, capable of almost 34 petaflops, was developed by China’s National University of Defense Technology (NUDT), is deployed at the National Supercomputer Center in Guangzho, and still enjoys the number-two position on the list.

• Linux vs. Windows Server OS Comparison [Ed: Worst article I saw today. A few characters cannot sum up the amount of nonsense in it.]

• The evolution of scalable microservices

  Today’s enterprise applications are deployed to everything from mobile devices to cloud-based clusters running thousands of multi-core processors. Users have come to expect millisecond response times and close to 100% uptime. And by “user” I mean both humans and machines. Traditional architectures, tools and products simply won’t cut it anymore. To paraphrase Henry Ford’s classic quote: we can’t make the horse any faster, we need cars for where we are going.

• The Evolution of the Standard COTS Server in Modern Data Centers [Ed: COTS is a buzzword that expands into two more buzzwords (that are in it); reject marketing slant that subjugates geeks and 'consumers']

  Standardization on x86 commercial off-the-shelf (COTS) servers within the data center has been a movement for some time because the architecture offers versatility, cost-savings, easier integrations, more attractive maintenance and management profiles, and, overall, a lower total cost of ownership than a proprietary hardware approach. But there are new requirements that are driving data center server choices these days, namely the need to support carrier virtualization, programmability, and the massive data sets that come with machine learning and advanced, real-time analytics.

• Federated Kubernetes with on-prem Clusters and Juju

  In this post we discus our efforts to setup a Federation of on-prem Kubernetes Clusters using CoreDNS. The Kubernetes Cluster version used is 1.6.2. We use Juju to deliver clusters on AWS, yet the clusters should be considered on-prem since they do not integrate with any of the cloud’s features. The steps described here are repeatable on any pool of resources you may have available (cloud or bare metal). Note that this is still a work in progress and should not be used on a production environment.

• [Older] CoreOS Brings Kubernetes-as-a-Service to Enterprise

  CoreOS today said it added features to its enterprise container-orchestration platform that include Kubernetes-as-a-service.

  The upcoming Tectonic 1.6.4 will allow enterprises to deploy and manage the latest version of upstream Kubernetes across bare metal, public-, private-, and hybrid-cloud environments. The container company says this gives enterprises the flexibility of running their applications on the cloud, without cloud vendor lock-in.