Category: Containers

Cloud-native software development with Virtual Application Networks

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Ted Ross, Red Hat

Communication between distributed software components in a cloud-native application is an important and challenging aspect of cloud-native development. This post introduces a solution to that problem using Virtual Application Networks (VANs).  A VAN can be set up by a developer and used to connect the components of an application that are deployed in different public, private, and edge cloud environments.

Cloud-native development is about writing software in such a way that it can be deployed easily, flexibly, and automatically into the hybrid-cloud ecosystem to take advantage of the scale of the cloud.  A big part of taking advantage of cloud scale is the ability to deploy components of a distributed system in different locations.

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Managing application and data portability at scale with Rook-Ceph

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Scott Creeley, Red Hat

One of the key requirements for Kubernetes in multi-cluster environments is the ability to migrate an application with all of its dependencies and resources from one cluster to another cluster. Application portability gives application owners and administrators the ability to better manage applications for common needs such as scaling out applications, high availability for applications, or just simply backing up applications for disaster recovery. This post is going to present one solution for enabling storage and data mobility in multicluster/hybrid cloud environments using Ceph and Rook.

Containerization and Container Native Storage has made it easier for developers to run applications and get the storage they need, but as this space evolves and matures it is becoming increasingly important to move your application and data around, from cluster to cluster and cloud to cloud.

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Kiali: An observability platform for Istio

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Dave Neary

Istio exists to make life easier for application developers working with Kubernetes. But what about making Istio easier? Well, that’s Kiali’s job. Read on to learn more about making Istio even more pleasant to use.
Deploying and managing microservice applications is hard. When you break down an application into components, you add complexity in how those components communicate with each other. Getting an alert when something goes wrong, and figuring out how to fix it, is a challenge involving networking, storage, and potentially dozens of different compute nodes.

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Scaling workload storage requirements across clusters

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Jon Cope

A number of multi-cloud orchestrators have promised to simplify deploying hundreds or thousands of high-availability services.  But this comes with massive infrastructure requirements. How could we possibly manage the storage needs of a thousand stateful processes?  In this blog, we’ll examine how we can leverage these orchestrators to address our dynamic storage requirements.

Currently in Kubernetes, there are two approaches in how a control plane can scale resources across multiple clusters.  These are commonly referred to as the Push and Pull models, referring to the way in which configurations are ingested by a managed cluster.  Despite being antonyms in name, these models are not mutually exclusive and may be deployed together to target separate problem spaces in a managed multi-cluster environment.

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Red Hat and NVIDIA bring scalable, efficient edge computing to smart cities

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Pete MacKinnon, Red Hat

David Bericat Lacima, Red Hat

Teams from Red Hat and NVIDIA have collaborated on creating a scalable hybrid cloud application that could revolutionize smart city initiatives such as traffic-flow monitoring and transportation management around the world. By working together, the two companies are creating solutions that make cities smarter and more efficient by taking sensor data and processing it in real-time to provide insights for traffic congestion, pedestrian flow, and infrastructure maintenance.

Running on top of the NVIDIA EGX platform with the NVIDIA GPU Operator, the application is built with NVIDIA’s Metropolis application framework for IoT that brings together innovative capabilities for real-time image processing where NVIDIA DeepStream SDK is used to extract metadata from live video streams at the edge. It then forwards the right metadata to the cloud for deeper analytical processing and further representation in an information dashboard depicted below.

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Passing Go: polyglot Kubernetes Operators

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Brian Proffitt

Operators within Kubernetes are useful tools, designed to extend the container orchestration platform with additional resources. More directly, an Operator, sometimes referred to as custom controllers, is a method of packaging, deploying, and managing a Kubernetes application. 

As useful as Operators are, they have had one limitation: originally they all had to be written in the Go programming language. Thanks to the Operator SDK, you do not need to develop your Operators in Go. The Operator SDK has options for Ansible and Helm that may be better suited for the way you or your team work. But, it can still be limiting for dev teams trying to build an operator if they don’t happen to be skilled in Helm or Ansible.

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Managing chaos in a containerized environment

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Brian Proffitt

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

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Mike Bursell, Red Hat

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

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Erin Boyd

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

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Anand Sanmukhani, Red Hat

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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