Kubernetes: Use cases and it's application in Industries

First of all we should have an idea what is Kubernetes and why do we need it and what it can do to reduce the workloads.

Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services, that facilitates both declarative configuration and automation. It has a large, rapidly growing ecosystem. Kubernetes services, support, and tools are widely available. It aims to provide a platform for automating deployment, scaling, and operations of application containers across clusters of hosts. It works with a range of container tools and runs containers in a cluster, often with images built using Docker. Many cloud services offer a Kubernetes-based platform or infrastructure as a service (PAAS or IAAS) on which Kubernetes can be deployed as a platform-providing service. Kubernetes provides:

• Service discovery and load balancing Kubernetes can expose a container using the DNS name or using their own IP address. If traffic to a container is high, Kubernetes is able to load balance and distribute the network traffic so that the deployment is stable.
• Storage orchestration Kubernetes allows you to automatically mount a storage system of your choice, such as local storages, public cloud providers, and more.
• Automated rollouts and rollbacks You can describe the desired state for your deployed containers using Kubernetes, and it can change the actual state to the desired state at a controlled rate. For example, you can automate Kubernetes to create new containers for your deployment, remove existing containers and adopt all their resources to the new container.
• Automatic bin packing You provide Kubernetes with a cluster of nodes that it can use to run containerized tasks. You tell Kubernetes how much CPU and memory (RAM) each container needs. Kubernetes can fit containers onto your nodes to make the best use of your resources.
• Self-healing Kubernetes restarts containers that fail, replaces containers, kills containers that don't respond to your user-defined health check, and doesn't advertise them to clients until they are ready to serve.
• Secret and configuration management Kubernetes lets you store and manage sensitive information, such as passwords, OAuth tokens, and SSH keys. You can deploy and update secrets and application configuration without rebuilding your container images, and without exposing secrets in your stack configuration.

Kubernetes defines a set of building blocks, which collectively provide mechanisms that deploy, maintain, and scale applications based on CPU, memory or custom metrics. Kubernetes is extensible to meet different workloads. This extensibility is provided in large part by the Kubernetes API, which is used by internal components as well as extensions and containers that run on Kubernetes. The platform exerts its control over compute and storage resources by defining resources as Objects, which can then be managed as such. Kubernetes follows the primary or replica architecture.

Some Case Studies of Kubernetes:

1. IBM: IBM cloud offers public, private, and hybrid cloud functionality across a diverse set of runtimes from its open whisk-based function as a service (FaaS) offering, managed Kubernetes and containers & 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. The work on this new service culminated with its public availability in the IBM Cloud in February 2018. Portieris (image trust service) 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.
2. Spotify: Spotify had containerized microservices running across its fleet of VMs with a homegrown container orchestration system called Helios. The biggest service currently running on Kubernetes takes about 10 million requests per second as an aggregate service and benefits greatly from autoscaling and according to spotify, teams would have to wait for an hour to create a new service and get an operational host to run it in production, but with Kubernetes, they can do that on the order of seconds and minutes. In addition, with Kubernetes's bin-packing and multi-tenancy capabilities, CPU utilization has improved on average two- to threefold.
3. CERN: CERN's technology team embraced containerization and cloud native practices, choosing Kubernetes for orchestration, Helm for deployment, Prometheus for monitoring, and CoreDNS for DNS resolution inside the clusters. Kubernetes federation has allowed the organization to run some production workloads both on premise and in public clouds. According to CERN engineer's, Kubernetes gives us the full automation of the application. It comes with built-in monitoring and logging for all the applications and the workloads that deploy in Kubernetes. The time to deploy a new cluster for a complex distributed storage system has gone from more than 3 hours to less than 15 minutes. Adding new nodes to a cluster used to take more than an hour and it takes less than 2 minutes. The time it takes to autoscale replicas for system components has decreased from more than an hour to less than 2 minutes. Initially, virtualization gave 20% overhead, but with tuning this was reduced to ~5%. Moving to Kubernetes on bare metal would get this to 0%. Not having to host virtual machines is expected to also get 10% of memory capacity back.
4. App Direct: The Kubernetes platform has helped support the engineering team’s 10x growth over the past few years. Moving to Kubernetes and services has meant that deployments have become much faster due to less dependency on custom-made, brittle shell scripts with SCP commands. Time to deploy a new version has shrunk from 4 hours to a few minutes. Additionally, the company invested a lot of effort to make things self-service for developers. Today, the company sees 1,600 deployments per week, compared to 1-30 before. The company also achieved cost savings by moving its marketplace and billing monoliths to Kubernetes from legacy EC2 hosts as well as by leveraging autoscaling, as traffic is higher during business hours.

Use cases of Kubernetes:

1. Learning Kubernetes by deploying a simple app

The first case where you can make use of Kubernetes may seem controversial, but still is very useful. Let’s assume that we have a simple three-tier application with backend written in Python/PHP, a database and front-end created in React or Angular. To deploy it, you can use Kubernetes. Yes, from a purely practical point of view this would be not very reasonable: Kubernetes is complex and creating a Kubernetes cluster to run one simple app would mean doing unnecessary work. Further, you can deploy such an app using other, less expensive solutions. But there is an educational purpose that shouldn’t be overlooked. In undertaking such a deployment, you will learn how to run a Kubernetes cluster and deploy applications on it.

2. Microservices architecture

A use case where you want to deploy a more complicated app with many components that will communicate with one another is a classic scenario for Kubernetes. In fact, its origins go back to Google deploying, managing and scaling apps in a more efficient way by using containers. That’s how the container orchestration platform Kubernetes was born. So, we now have a K8s cluster with one complicated app deployed. This app has numerous components that communicate with one another. Kubernetes helps you manage this communication. It handles for developers such tasks as detecting problems with communication between the intra-app components, managing the behaviour of components in the event of a failure or managing the authentication processes between components. What’s more, as more or less resources are needed for a particular component, Kubernetes automatically scales them up or down. This is a clear advantage of the microservice architecture: scalability. You can scale a single component rather than the whole app. Kubernetes has built in tools like Horizontal Pod Autoscaler, which helps ensure that each microservice has the optimal number of replicas.

3. Lift and shift – from servers to cloud

This scenario occurs frequently today, as software is migrated from on-prem infrastructure to cloud solutions. Let’s imagine a situation where we have an application deployed on physical servers in a classical data center. For practical or economic reasons, it has been decided to move it to the cloud: either to a Virtual Machine or to big pods in Kubernetes. Of course, moving it to big pods in K8s isn’t a cloud native approach, but it can be treated as an intermediary phase. First, such a big app working outside the cloud is moved to the same big app in Kubernetes. It is then split into smaller components to become a regular cloud native-app. Such methodology is called “lift and shift” and is a good use case where Kubernetes can be used effectively.

4. Cloud-native Network Functions (CNF)

A few years ago, big telco companies had a problem. Their network services were based on hardware such as firewalls or load balancers provided by specialized hardware companies. Of course, this left them dependent on the hardware providers, and gave them little in the way of flexibility. If new functionality was needed, operators had to upgrade existing hardware. When a device firmware update was not possible, additional hardware had to be purchased. To address this disadvantage, the telcos opted to have all these network services as software and use Virtual Machines and OpenStack for network function virtualization (NFV). They now want to go a step further and use containers for the same purpose. This approach is called Cloud-native Network Functions (CNF).

5. Machine learning and Kubernetes

Machine learning techniques are now widely used to solve real-life problems. Successes have come in multiple fields–self-driving cars, image recognition, machine translation, speech recognition, game playing. Machine learning models have beaten even humans in games like Go, which was once thought to be too difficult a game for machines to crack. Moreover, AI could lead to real breakthroughs in detecting cancer and drug discovery. As you can see, it takes time to use an AI-trained model in an application. Therefore, many companies would like to simplify this process and make the life of data scientists or ML engineers easier by introducing a toolkit to speed up the whole process. In this way, the number of operations necessary to deploy such an app will be significantly reduced, shortening the app’s time-to-market. In this scenario, enterprises can harness the power of Kubernetes, as all the calculations necessary to train the ML model are performed inside the K8s cluster. The data scientist or ML engineer will only need to clean the data and write the code. The rest will be handled by a toolkit based on Kubernetes such as Kubeflow by Google and CodiLime spin-off Neptune. The increasing demand for AI-powered solutions will surely further promote the adoption of Kubernetes.

7. CI/CD – software development lifecycle

Kubernetes also brings considerable benefits to Continuous Integration/Continuous Deployment or Continuous Delivery methodology. Once an app is deployed into operations, how it works must be monitored constantly. That’s in addition to gathering users feedback and developing new features. Whether it’s for testing, frequent releases or deploying newer versions of an app, Kubernetes makes everything simpler and more manageable.