Kubernetes Use Case
Kubernetes is all the rage these days, and rightly so. The open source project, originally created by Google, is designed to automate the deployment and management of your containerized applications.
What is Kubernetes ?
Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications. It enables users to consolidate containers for management and discovery, examine the status of rollout and test global enterprises, monitor application deployment, create and destroy Pods dynamically, update and query the API server for endpoints, create instances with a service definition, consume abstract storage resources, retrieve Metrics API with the monitoring tools, customize and update orchestration.
Rise of Kubernetes
First released in 2014, Originally created by Google and then donated to Cloud Native Computing Foundation. As an open-source product, it is available on various platforms and systems. Google Cloud ( GKE ), Microsoft Azure ( AKS ), and Amazon AWS ( EKS ) offer official support for Kubernetes, so configuration changes to the cluster itself are not necessary.
The popularity of Kubernetes has steadily increased, with more than four major releases in 2017. K8s also was the most discussed project in GitHub during 2017, and was the project with the second most reviews.
“Kubernetes has the opportunity to be the new cloud platform. The amount of innovation that’s going to come from being able to standardize on Kubernetes as a platform is incredibly exciting — more exciting than anything I’ve seen in the last 10 years of working on the cloud.”
Companies Currently using Kubernetes
Top Industries that use Kubernetes
Top Countries that use Kubernetes
Kubernetes Case Study
Airbnb’s Kubernetes story
Airbnb’s transition from a monolithic to a microservices architecture is pretty amazing. They needed to scale continuous delivery horizontally, and the goal was to make continuous delivery available to the company’s 1000 or so engineers so they could add new services. Airbnb adopted to support over 1000 engineers concurrently configuring and deploying over 250 critical services to Kubernetes (at a frequency of about 500 deploys per day on average). I want you to see this excellent presentation from Melanie Cebula, the infrastructure engineer at Airbnb.
IBM Case Study
Challenge
IBM Cloud offers public, private, and hybrid cloud functionality across a diverse set of runtimes from its OpenWhisk-based function as a service (FaaS) offering, managed Kubernetes and containers, to Cloud Foundry platform as a service (PaaS). These runtimes are combined with the power of the company’s enterprise technologies, such as MQ and DB2, its modern artificial intelligence (AI) Watson, and data analytics services. Users of IBM Cloud can exploit capabilities from more than 170 different cloud native services in its catalog, including capabilities such as IBM’s Weather Company API and data services. In the later part of 2017, the IBM Cloud Container Registry team wanted to build out an image trust service.
Solution
The work on this new service culminated with its public availability in the IBM Cloud in February 2018. The image trust service, called Portieris, is fully based on the Cloud Native Computing Foundation (CNCF) open source project Notary, according to Michael Hough, a software developer with the IBM Cloud Container Registry team. Portieris is a Kubernetes admission controller for enforcing content trust. Users can create image security policies for each Kubernetes namespace, or at the cluster level, and enforce different levels of trust for different images. Portieris is a key part of IBM’s trust story, since it makes it possible for users to consume the company’s Notary offering from within their IKS clusters. The offering is that Notary server runs in IBM’s cloud, and then Portieris runs inside the IKS cluster. This enables users to be able to have their IKS cluster verify that the image they’re loading containers from contains exactly what they expect it to, and Portieris is what allows an IKS cluster to apply that verification.
Impact
IBM’s intention in offering a managed Kubernetes container service and image registry is to provide a fully secure end-to-end platform for its enterprise customers. “Image signing is one key part of that offering, and our container registry team saw Notary as the de facto way to implement that capability in the current Docker and container ecosystem,” Hough says. The company had not been offering image signing before, and Notary is the tool it used to implement that capability. “We had a multi-tenant Docker Registry with private image hosting,” Hough says. “The Docker Registry uses hashes to ensure that image content is correct, and data is encrypted both in flight and at rest. But it does not provide any guarantees of who pushed an image. We used Notary to enable users to sign images in their private registry namespaces if they so choose.”
OpenAI Case Study
Challenge
An artificial intelligence research lab, OpenAI needed infrastructure for deep learning that would allow experiments to be run either in the cloud or in its own data center, and to easily scale. Portability, speed, and cost were the main drivers.
Solution
OpenAI began running Kubernetes on top of AWS in 2016, and in early 2017 migrated to Azure. OpenAI runs key experiments in fields including robotics and gaming both in Azure and in its own data centers, depending on which cluster has free capacity. “We use Kubernetes mainly as a batch scheduling system and rely on our autoscaler to dynamically scale up and down our cluster,” says Christopher Berner, Head of Infrastructure. “This lets us significantly reduce costs for idle nodes, while still providing low latency and rapid iteration.”
Impact
The company has benefited from greater portability: “Because Kubernetes provides a consistent API, we can move our research experiments very easily between clusters,” says Berner. Being able to use its own data centers when appropriate is “lowering costs and providing us access to hardware that we wouldn’t necessarily have access to in the cloud,” he adds. “As long as the utilization is high, the costs are much lower there.” Launching experiments also takes far less time: “One of our researchers who is working on a new distributed training system has been able to get his experiment running in two or three days. In a week or two he scaled it out to hundreds of GPUs. Previously, that would have easily been a couple of months of work.”
Problem Solved Using Kubernetes
- Automatic deployment of application services
- Automatic configuration of application network
- Automatic distribution of services across infrastructure
- Automatic resource allocation for application services
- Automatic load balancing between application components
- Automatic replication of application components
- Automatic failover of application components
- Easily scale out application services when required
- Sharing of storage across multiple containers
Conclusion
Around the world, many CIO’s and technologists have chosen to use Kubernetes, and it is expected to evolve much more in the years to come.
Containers are becoming more and more popular in the software world and Kubernetes has become the industry standard for deploying containers in production. We will expect a high growth rate of Kubernetes this year too.