Kubernetes and Case studies on it
By: Yogesh Kumar Prajapati
Tags: Case Study, Kubernetes, Kubernetes Cluster

In this article, we are going to see what is Kubernetes and how it helps industries to grow their productivity.

What is Kubernetes:-

Kubernetes is a Container Orchestration engine that is used to manage the docker container. When we launch the container and due to any reason if it got crashed or deleted or stopped so we need some tool that can continuously monitor and up the container so we have k8s there.

Kubernetes makes sure services are running smoothly just like the developer wants.

Kubernetes Features:-

Automated Rollouts and Rollbacks:-

Kubernetes progressively rolls out changes to your application or its configuration, while monitoring application health to ensure it doesn’t kill all your instances at the same time. If something goes wrong, Kubernetes will rollback the change for you. Take advantage of a growing ecosystem of deployment solutions.

Service Discovery and Load Balancing:-

No need to modify your application to use an unfamiliar service discovery mechanism. Kubernetes gives Pods their own IP addresses and a single DNS name for a set of Pods and can load-balance across them.

Service Topology:-

Routing of service traffic based upon cluster topology.

Self Healing:-

Restarts containers that fail, replace, and reschedules containers when nodes die, kills containers that don’t respond to your user-defined health check and don’t advertise them to clients until they are ready to serve.

Secret and Configuration Management:-

Deploy and update secrets and application configuration without rebuilding your image and without exposing secrets in your stack configuration

The architecture of Kubernetes:-

Kubernetes contains master-slave architecture. It can have multiple masters and multiple slaves that is also known as master-slave architecture

A Kubernetes cluster consists of a set of worker machines, called nodes, that run containerized applications. Every cluster has at least one worker node.

The worker node(s) host the Pods that are the components of the application workload. The control plane manages the worker nodes and the Pods in the cluster. In production environments, the control plane usually runs across multiple computers and a cluster usually runs multiple nodes, providing fault-tolerance and high availability.

Case Studies: Babylon

Challange:-

A large number of Babylon’s products leverage machine learning and artificial intelligence, and in 2019, there wasn’t enough computing power in-house to run a particular experiment. The company was also growing (from 100 to 1,600 in three years) and planning expansion into other countries.

Solution:-

Babylon had migrated its user-facing applications to a Kubernetes platform in 2018, so the infrastructure team turned to Kubeflow, a toolkit for machine learning on Kubernetes. “We tried to create a Kubernetes core server, we deployed Kubeflow, and we orchestrated the whole experiment, which ended up being a really good success,” says AI Infrastructure Lead Jérémie Vallée. The team began building a self-service AI training platform on top of Kubernetes.

Impact:-

Instead of waiting hours or days to be able to compute, teams can get access instantaneously. Clinical validations used to take 10 hours; now they are done in under 20 minutes. The portability of the cloud native platform has also enabled Babylon to expand into other countries.

Kubernetes is a great platform for machine learning because it comes with all the scheduling and scalability that you need. — JÉRÉMIE VALLÉE, AI INFRASTRUCTURE LEAD AT BABYLON “G iving a Kubernetes-based platform to our data scientists has meant increased security, increased innovation through empowerment, and a more affordable health service as our cloud engineers are building an experience that is used by hundreds on a daily basis, rather than supporting specific bespoke use cases.” — JEAN MARIE FERDEGUE, DIRECTOR OF PLATFORM OPERATIONS AT BABYLON

Case Studies: Addidas

Staying True to Its Culture, adidas Got 40% of Its Most Impactful Systems Running on Kubernetes in a Year

Challange:-

In recent years, the adidas team was happy with its software choices from a technology perspective — but accessing all of the tools was a problem. For instance, “just to get a developer VM, you had to send a request form, give the purpose, give the title of the project, who’s responsible, give the internal cost center a call so that they can do recharges,” says Daniel Eichten, Senior Director of Platform Engineering. “The best case is you got your machine in half an hour. Worst case is half a week or sometimes even a week.”

Solution:-

To improve the process, “we started from the developer point of view,” and looked for ways to shorten the time it took to get a project up and running and into the adidas infrastructure, says Senior Director of Platform Engineering Fernando Cornago. They found the solution with containerization, agile development, continuous delivery, and a cloud native platform that includes Kubernetes and Prometheus.

Impact:-

Just six months after the project began, 100% of the adidas e-commerce site was running on Kubernetes. Load time for the e-commerce site was reduced by half. Releases went from every 4–6 weeks to 3–4 times a day. With 4,000 pods, 200 nodes, and 80,000 builds per month, adidas is now running 40% of its most critical, impactful systems on its cloud native platform.