Managing chaos in a containerized environment

Quick, name some weird stuff that’s happened to your production machines.

Accidentally dropping a production database table? Rolling out a patch that enabled any user to log in with any password? Disabling a load balancer? Using a dictionary to physically keep keyboard keys depressed so “terminals [could] repeatedly [hit] the enter key in order for the logins and print jobs of about 40,000 people to work”?

It’s happened to Alex Corvin, a senior engineer at Red Hat. Well, not that last one. But Corvin has been around long enough in his career to have met Mr. Murphy and his Law: if it can go wrong, it will.

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Trust No One, Run Everywhere–Introducing Enarx

When you run a workload as a VM, container or in a serverless environment, that workload is vulnerable to interference by any person or software with hypervisor, root or kernel access.  Enarx, a new open source project,  aims to make it simple to deploy workloads to a variety of trusted execution environments (TEEs) in the public cloud, on your premises or elsewhere, and to ensure that your application workload is as secure as possible.

When you run your workloads in the cloud, there are no technical barriers to prevent  the cloud providers–or their employees–from looking into your workloads, peeking into the data, or even changing the running process.  That’s because when you run a workload as a VM, container or serverless, the way that these are implemented means that a person or software entity with sufficient access can interfere with any process running on that machine.

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Understanding and Applying Storage Federation Patterns Using KubeFed

As a cloud user, how do you avoid the pull of data gravity of one provider or another? How can you get the flexibility and tooling to migrate your infrastructure and applications as your needs change? How do you get to the future of storage federation as data agility?

In this blog we cover the primary motivations and considerations that drive the enablement of flexible, scalable, and agile data management. Our subsequent blogs cover practical use cases and concrete solutions for our 6 federated storage patterns. 

All of this is grounded in Red Hat’s work to take a lead in multi-cluster enablement and hybrid cloud capabilities. Our work is focused on leading and moving forward projects that lay the groundwork for this vision in OpenShift, driven via the Kubernetes project KubeFed.

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Building a Scalable TensorFlow Twitter Bot for Red Hat Summit

Red Hat’s AI Center of Excellence and PerceptiLabs wanted a way to demonstrate a TensorFlow model to the public during the 2019 Red Hat Summit. The plan was for this model to take images as input, and then respond with the likelihood of a Red Hat fedora being in that image. Here’s what we learned during Red Hat Summit.

This application, which we called Fedora Finder Bot, would be featured during Red Hat CTO Chris Wright’s keynote, where PerceptiLabs demoed their AI platform.

Our initial solution for this objective would be a Twitter bot that receives tweets or direct messages and replies with the output from the TensorFlow model. Twitter being a public service, we felt it could make the model available to a large number of users, so that any user could just tweet to the bot with a picture and the bot would respond with the model’s output.

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Rook Changes the Kubernetes Storage Landscape

It’s no secret that if you want to run containerized applications in a distributed way, then Kubernetes is the platform for you. Kubernetes’ role as an orchestration platform for containers has taken center stage to become a main player for automating deployment, scaling, and management of applications within containers. Red Hat’s own OpenShift Container Platform is a Kubernetes distribution that uses Kubernetes optimized for enterprises.

Storage has been one of the areas of potential optimization. Many containers, by their very nature, are usually small enough to be easily distributed and managed. Containers hold applications, but the data those applications use needs to be held somewhere else, for a number of reasons. Of particular interest in this post, we want to avoid the containers themselves becoming too large and unwieldy to be effectively managed.

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Next Generation Tools for Container Technology

In this video from the 2018 Red Hat Summit, Dan Walsh and Mrunal Patel lead a journey through a set of next generation tools for creating, deploying, and maintaining containers.

This journey covers tools such as CRI-O, Buildah, and Skopeo, which are being developed with other tools by Red Hat and the community into a complete toolchain for developing, operating, and maintaining Open Container Initiative (OCI)-compliant containers.

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Kubernetes and the Platform of the Future

In another installment from the Red Hat Summit track from the Office of the CTO, this video is an informal discussion between Brandon Philips (previously CTO of CoreOS, acquired by Red Hat) and Clayton Coleman (Chief Engineer for OpenShift), interviewed by Steve Watt. They focus on Kubernetes as a platform of the future, identifying interesting trends in the open source ecosystem.

This discussion is a good example of the type of technologists that comprise the modern open source ecosystem, and epitomized by these three from Red Hat. Their backgrounds in real world development and operations combines with a genuine desire to help people that fuels their work in open source communities and product creation.

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The Future of Storage in Container Space: Part 4

The challenges of maintaining persistent storage in environments that are anything but persistent should not be taken lightly. My recent conversation with Ceph founder Sage Weil certainly made that clear. Thus far, the conversation with Sage has highlighted key areas of focus for the Red Hat Storage team as they look to the horizon, including how storage plans are affected by:

  • Hardware trends (examined in Part 1)
  • Software platforms (reviewed in Part 2)
  • Multi-cloud and hybrid cloud (discussed in Part 3)

In the last segment of our interview, Sage focused on technology that’s very much on the horizon: the emerging workloads. Specifically, how will storage work in a world where artificial intelligence and machine learning begins to shape software, hardware, and networking architecture?

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The Future of Storage in Container Space: Part 3

It was not that long ago when organizations had in-house servers humming along running applications and storing data. Today, the opportunity afforded by containers means that applications can now live on a cloud platform (either public or private), or one of several available cloud platforms.

But while applications and microservices housed in stateless containers are easy to move from place to place (indeed, that’s a big part of the appeal of containers), the data the applications are accessing are stateful and very, very difficult to relocate while still maintaining consistency, latency, and throughput. This is one of the challenges faced by the Red Hat Storage team, and  addressed by Sage Weil in his recent presentation at Red Hat Summit: maintaining data availability with acceptable latency when working with applications in multi-cloud and hybrid cloud environments.

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Clouds Today, Serverless Tomorrow: Your Future Apps and Platforms

When we look to the future of applications and platforms, we need to keep an eye on the solutions of the past.

That is one of the main theses of Stephanos Bacon, Sr. Director of Portfolio Strategy at Red Hat, in this video from Red Hat Summit 2018, “Clouds Today, Serverless Tomorrow: Your Future Apps and Platforms”:

In order to understand the present situation around the many choices of languages and platforms a developer faces, Stephanos briefly walks through a 25 year year journey of enterprise software development. This journey is one of a “continuous-though-forward-moving cycle”.

This cycle looks back at itself to learn and adapt from the past while moving forward in response to changing market imperatives.  While we may need new solutions, we also reach back in time to find seemingly old solutions that address new classes of problems.

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