At last month’s KubeCon in Seattle, members of the Cloud Native Computing Foundation put forth a chart depicting the various projects, both commercial and open source, that either individually or collectively contributed to its perception of the “cloud native” ecosystem. You might call it, for lack of a more original phrase, a new stack.
It’s being called the Cloud Native Landscape (CNL), its version number of 0.9.2 suggesting it’s close to finished, but not quite. It looks like a chart one might print up of the various vendors at a gaming conference, or of the vendor charts at one of the early computer shows. Remember when hotel meeting rooms were divided into aisles, and the first software developers shouted over one another to get attention?
Supercharged hardware will speed up deep learning in everything from tiny devices to massive data centers.
Last March, Google’s computers roundly beat the world-class Go champion Lee Sedol, marking a milestone in artificial intelligence. The winning computer program, created by researchers at Google DeepMind in London, used an artificial neural network that took advantage of what’s known as deep learning, a strategy by which neural networks involving many layers of processing are configured in an automated fashion to solve the problem at hand.
Unknown to the public at the time was that Google had an ace up its sleeve. You see, the computers Google used to defeat Sedol contained special-purpose hardware—a computer card Google calls its Tensor Processing Unit.
IT pros and developers can’t be excellent at everything. Instead, embracing specialization and empowering collaboration in your organization can achieve meaningful and lasting DevOps progress.
Building a collaborative and lightning-quick DevOps organization is a complex but critical business mission today. The technology industry is filled with a host of best practices that promise to help companies achieve this objective. Some of these suggestions make a great deal of sense. But many of the recommendations lead to mediocre results, and organizations simply can’t afford to be half-good when it comes to DevOps.
The container craze will turn four next year. Yes, Linux containers have been around longer than that, but the rise of Docker—first released to the public on March 20, 2013—has sparked the surge of interest we’re riding right now.
It’s a fascinating adolescent phase, as containers not only roll into production but also get acclimated to enterprise needs and bigger-money investors. Here’s a glance at the major themes that surrounded containers in 2016 and are likely to continue into 2017.
Scheduler ‘Wars’
Some players are rushing to crown Kubernetes as the de facto standard for container orchestration. Others are just getting started with Kubernetes alternatives.
The Cloud Native Computing Foundation is taking to the road February 7-9 in Portland, Seattle and Vancouver to offer end users, developers, students and other community members the ability to learn from experts at Red Hat, Apprenda and CNCF on how to use Kubernetes and other cloud native technologies in production. Sponsored by Intel and Tigera, the first ever Cloud Native/Kubernetes 101 Roadshow: Pacific Northwestwill introduce key concepts, resources and opportunities for learning more about cloud native computing.
The CNCF roadshow series focuses on meeting with and catering to those using cloud native technologies in development, but not yet in production.Cities and locations include:
Each roadshow will be held from 2-5pm, with the full agenda including presentations from:
Dan Kohn, Executive Director of the Cloud Native Computing Foundation. Dan will discuss:
What is cloud native computing — orchestrated containers as part of a microservices architecture — and why are so many cloud users moving to it instead of virtual machines
An overview of the CNCF projects — Kubernetes, Prometheus, OpenTracing and Fluentd — and how we as a community are building maps through previously uncharted territory
A discussion of top resources for learning more, including Kubernetes the Hard Way, Kubernetes bootcamp, and CloudNativeCon/KubeCon and training and certification opportunities
Brian Gracely, Director of Product Strategy at Red Hat. Brian will discuss:
Real-world use of Kubernetes in production today at Amadeus, LeShop, Produban/Santander & FICO
Why contributing to CNCF-hosted projects should matter to you
How cross-community collaboration is the key to the success of the future of Cloud Native
Isaac Arias, Technology Executive, Digital Business Builder, and Passionate Entrepreneur at Apprenda. Isaac will discuss:
Brief history of machine abstractions: from VMs to Containers
Why containers are not enough: the case for container orchestration
From Borg to Kubernetes: the power of declarative orchestration
Kubernetes concepts and principles and what it takes to be Cloud Native
By the end of this event, attendees will understand how cloud users are implementing cloud native computing — orchestrated containers as part of a microservices architecture – instead of virtual machines. Real-world Kubernetes use cases at Amadeus, LeShop, Produban/Santander, and FICO will be presented. A detailed walk through of Prometheus (monitoring system), OpenTracing (tracing standard) and Fluentd (logging) projects and each level of the stack will also be provided.
Each city is limited in space, so sign up now! Use the code MEETUP50 to receive 50% off registration!
In 2017, The Linux Foundation’s Embedded Linux Conference marks its 12th year as the premier vendor-neutral technical conference for companies and developers using Linux in embedded products.
Now co-located with OpenIoT Summit, ELC promises to be the best place for embedded and application developers, product vendors, kernel and systems developers as well systems architects and firmware developers to learn, share and advance the technical work required for embedded Linux and IoT.
In anticipation of this year’s North America event, to be held Feb. 21-23 in Portland, Oregon, we rounded up the top videos from the 2017 ELC and OpenIoT Summit. Register now with the discount code, LINUXRD5, for 5% off the registration price.Save over $150 by registering before January 15, 2017.
Several home automation platforms support Python as an extension, but if you’re a real Python fiend, you’ll probably want Home Assistant, which places the programming language front and center. Paulus Schoutsen created Home Assistant in 2013 “as a simple script to turn on the lights when the sun was setting,” as he told attendees of his recent Embedded Linux Conference and OpenIoT Summit presentation, “Automating your Home with Home Assistant: Python’s Answer to the Internet of Things.”
Schoutsen, who works as a senior software engineer for AppFolio in San Diego, has attracted 20 active contributors to the project. Home Assistant is now fairly mature, with updates every two weeks and support for more than 240 different smart devices and services. The open source (MIT license) software runs on anything that can run Python 3 — from desktop PCs to a Raspberry Pi, and counts thousands of users around the world.
Linus Torvalds, the creator and lead overseer of the Linux kernel, and “the reason we are all here,” in the words of his interviewer, Intel Chief Linux and Open Source Technologist Dirk Hohndel, was upbeat about the state of Linux in embedded and Internet of Things applications. Torvalds’ very presence signaled that embedded Linux, which has often been overshadowed by Linux desktop, server, and cloud technologies, has come of age.
“Maybe you won’t see Linux at the IoT leaf nodes, but anytime you have a hub, you will need it,” Torvalds told Hohndel. “You need smart devices especially if you have 23 [IoT standards]. If you have all these stupid devices that don’t necessarily run Linux, and they all talk with slightly different standards, you will need a lot of smart devices. We will never have one completely open standard, one ring to rule them all, but you will have three of four major protocols, and then all these smart hubs that translate.”
It turns out that software — and computer education curricula — have not always kept up with new developments in hardware, ARM Ltd. kernel developer Mark Rutland said in his presentation “Stale Data, or How We (Mis-)manage Modern Caches.”
“Cache behavior is surprisingly complex, and caches behave in subtly different ways across SoCs,” Rutland told the ELC audience. “It’s very easy to misunderstand the rules of how caches work and be lulled into a false sense of security.”
Speaking shortly after the release of Open Connectivity Foundation (OCF)’s IoTivity 1.1, Vijay Kesavan, a Senior Member of Technical Staff in the Communication and Devices Group at Intel Corp, told the ELC audience about plans to support new platforms and IoT ecosystems in v2.0. He also explained how the OCF is exploring usage profiles beyond home automation in domains like automotive and industrial.
Sometimes the best tutorials come not from experts, but from proficient newcomers who are up to date on the latest entry-level technologies and can remember what it’s like to be a newbie. It also helps if, like Grant Likely, the teacher is a major figure in embedded Linux who understands how hardware is ignited by software.
At the Embedded Linux Conference, Likely — who is a Linux kernel engineer and maintainer of the Linux Device Tree subsystem used by many embedded systems — described his embedded hardware journey in a presentation called “Hardware Design for Linux Engineers” — or as he put it, “explaining stuff I only learned six months ago.”
https://www.youtube.com/watch?v=kCH5QkmKArg
Linux.com readers can register now with the discount code, LINUXRD5, for 5% off the registration price. Save over $150 by registering before January 15, 2017.
Thomas Di Giacomo, Chief Technology Officer at SUSE started his LinuxCon Europe keynote with a brief clip in the style of Mr. Robot where in 2016 even Evil Corp has gone open source.
The 25th anniversary of Linux was a big milestone celebrated by many of us at LinuxCon events throughout the year, and it was a theme throughout many of the presentations. Thomas Di Giacomo, Chief Technology Officer at SUSE started his LinuxCon Europe keynote with a brief clip in the style of Mr. Robot where in 2016 even Evil Corp has gone open source and we have won. He says that “open source is seen as a technology savior. That’s why companies have been embracing it, because they have to, to remain viable.”
Giacomo compares the human brain at age 25 with Linux. He talks about how at 25 years old, neurologists say that our brains make a big leap in maturity when the prefrontal cortex becomes fully operational, which helps us focus, make more logical decisions, make more complex plans, be more organized, and be more disciplined. At 25, Linux is also maturing in all of these ways, which leads us to collaborate more together to achieve great things.
Going back a few centuries to around the time of Leonardo Da Vinci before science was fully mature, there were individual bright minds active in poetry, philosophy, and other arts and domains, who could cover most of their contemporary knowledge. Giacomo described them “as men who knew it all, people like Aristotle, Roger Bacon, Da Vinci, Kepler, Humboldt, and others.”
Technology today is much too complex to be understood by a single person. “The future of open source is about contributing together more and more, so that we can achieve more and more complex challenges. We should try to scale our individual brains into a much larger collective, connected, and functional brain,” Giacomo says.
This is similar to the Avengers. Individual super heroes acting alone couldn’t save the day, so they are teaming up to be stronger together and more powerful than the sum of their parts.
Giacomo suggests that “in our community too, we have gone from individual mighty Hulks to groups of Avengers, so … we have to work more and more together to keep fixing more and more challenging problems. … Simply having the code available is not enough to ensure long-term viability of open source. We also need to make sure we keep working on fostering inclusive environments where everyone can contribute, so that the open source momentum continues and grows for the next decades to come.”
This was a fun talk that can be best appreciated by watching the entire video!
Interested in speaking at Open Source Summit North America on September 11 – 13? Submit your proposal by May 6, 2017. Submit now>>
Not interested in speaking but want to attend? Linux.com readers can register now with the discount code, LINUXRD5, for 5% off the all-access attendee registration price. Register now to save over $300!
What are micro operating systems and why should individuals and organizations focused on the cloud care about them? In the cloud, performance, elasticity, and security are all paramount. A lean operating system that facilitates simple server workloads and allows for containers to run optimally can serve each of these purposes. Unlike standard desktop or server operating systems, the micro OS has a narrow, targeted focus on server workloads and optimizing containers while eschewing the applications and graphical subsystems that cause bloat and latency.
In fact, these tiny platforms are often called “container operating systems.” Containers are key to the modern data center and central to many smart cloud deployments. According to Cloud Foundry’s report “Containers in 2016,” 53 percent of organizations are either investigating or using containers in development and production. The micro OS can function as optimal bedrock for technology stacks incorporating tools such as Docker and Kubernetes.
The Linux Foundation recentlyreleased its 2016 report“Guide to the Open Cloud: Current Trends and Open Source Projects.” This third annual report provides a comprehensive look at the state of open cloud computing. You candownload the report now, and one of the first things to notice is that it aggregates and analyzes research, illustrating how trends in containers, microservices, and more shape cloud computing. In fact, from IaaS to virtualization to DevOps, the report provides descriptions and links to categorized projects central to today’s open cloud environment.
In this series of posts, we will look at many of these projects, by category, providing extra insights on how the overall category is evolving. Below, you’ll find a collection of micro or “minimalist” operating systems and the impact that they are having, along with links to their GitHub repositories, all gathered from the Guide to the Open Cloud:
Project Atomic is Red Hat’s umbrella for many open source infrastructure projects to deploy and scale containerized applications. It provides an operating system platform for a Linux Docker Kubernetes (LDK) application stack, based on Fedora, CentOS and Red Hat Enterprise Linux. Project Atomic on GitHub
A lightweight Linux operating system designed for clustered deployments providing automation, security, and scalability for containerized applications. It runs on nearly any platform whether physical, virtual, or private/public cloud. CoreOS on GitHub
Photon OS is a minimal Linux operating system for cloud-native apps optimized for VMware’s platforms. It runs distributed applications using containers in multiple formats including Docker, Rkt, and Garden. Photon on GitHub
RancherOS is a minimalist Linux distribution for running Docker containers. It runs Docker directly on top of the kernel, replacing the init system, and delivers Linux services as containers. RancherOS on GitHub
This is the first entry in an insideHPC series that delves into in-memory computing and the designs, hardware and software behind it. This series, compiled in a complete Guide available here, also covers five ways in-memory computing can save money and improve TCO.
To achieve high performance, modern computer systems rely on two basic methodologies to scale resources. Each design attempts to bring more processors (cores) and memory to the user. A scale-up design that allows multiple cores to share a large global pool of memory is the most flexible and allows large data sets to take advantage of full in-memory computing. A scale-out design distributes data sets across the memory on separate host systems in a computing cluster. Although the scale-out cluster often has a lower hardware acquisition cost, the scale-up in-memory system provides a much better total cost of ownership (TCO) based on the following advantages:
