Kubernetes usage in Industries and the use cases

Kubernetes was first released in 2014,is an open-source container orchestration tool that can automatically scale, distribute and manage fault tolerance on containers. Originally created by Google and then donated to Cloud Native Computing Foundation, Kubernetes is widely used in production environments to handle Docker containers and other container tools in a fault-tolerant manner. As an open-source product, it is available on various platforms and systems. Google Cloud, Microsoft Azure, and Amazon AWS 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

Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications. It groups containers that make up an application into logical units for easy management and discovery. Kubernetes builds upon 15 years of experience of running production workloads at Google, combined with best-of-breed ideas and practices from the community.

Kubernetes clusters can span hosts across on-premise, public, private, or hybrid clouds. For this reason, Kubernetes is an ideal platform for hosting cloud-native applications that require rapid scaling, like real-time data streaming through Apache Kafka.

Kubernetes was originally developed and designed by engineers at Google. Google was one of the early contributors to Linux container technology and has talked publicly about how everything at Google runs in containers. (This is the technology behind Google’s cloud services.)

Amazon EKS Is A Fully Managed Kubernetes Service That Is Secure, Reliable & Scalable. Applications Managed By Amazon EKS Are Fully Compatible with Kubernetes Environments. Continuous Delivery. Easy Migration. App Load-Balancing.

Kubernetes is often chosen for the following reasons:

• Kubernetes has a better infrastructure than many of the DevOps tools
• Kubernetes breaks down containers into smaller modules to enable more granular management
• Kubernetes deploys software updates often and seamlessly
• Kubernetes lays the foundation for cloud-native apps

Kubernetes Architecture

Kubernetes has two nodes—Master Node and Server Node.

Master

The master node is the most vital component of Kubernetes architecture. It is the entry point of all administrative tasks. There is always one node to check for fault tolerance.

The master node has various components, such as:  

• ETCD
• Controller Manager 
• Scheduler
• API Server
• Kubectl

1. ETCD

• This component stores the configuration details and essential values
• It communicates with all other components to receive the commands to perform an action.
• Manages network rules and post-forwarding activity

2. Controller Manager

• A daemon (server) that runs in a continuous loop and is responsible for gathering information and sending it to the API Server
• Works to get the shared set of clusters and change them to the desired state of the server 
• The key controllers are the replication controllers, endpoint controller, namespace controllers, and service account controllers
• The controller manager runs controllers to administer nodes and endpoints

3. Scheduler

• The scheduler assigns the tasks to the slave nodes
• It is responsible for distributing the workload and stores resource usage information on every node
• Tracks how the working load is used on clusters and places the workload on available resources.

4. API Server

• Kubernetes uses the API server to perform all operations on the cluster
• It is a central management entity that receives all REST requests for modifications, serving as a frontend to the cluster
• Implements an interface, which enables different tools and libraries to communicate effectively

5. Kubectl

• Kubectl controls the Kubernetes cluster manager

    Syntax - kubectl [flags]

Slave

The slave node has the following components:

1. Pod

• A pod is one or more containers controlled as a single application
• It encapsulates application containers, storage resources, and is tagged by a unique network ID and other configurations that regulate the operation of containers

2. Docker

• One of the basic requirements of nodes is Docker
• It helps run the applications in an isolated, but lightweight operating environment. It runs the configured pods
• It is responsible for pulling down and running containers from Docker images

3. Kubelet

• Service responsible for conveying information to and from to the control plane service
• It gets the configuration of a pod from the API server and ensures that the containers are working efficiently
• The kubelet process is responsible for maintaining the work status and the node server

4. Kubernetes Proxy

• Acts as a load balancer and network proxy to perform service on a single worker node
• Manages pods on nodes, volumes, secrets, the creation of new containers, health check-ups, etc.
• A proxy service that runs on every node that makes services available to the external host.

Deploying Kubernetes

Kubernetes offers a new way to deploy applications using containers. It creates an abstraction layer which can be manipulated with declarative rather than imperative programming. This way, it is much simpler to deploy and upgrade services over time. The screenshot below shows the deployment of a replication controller which controls the creation of pods—the smaller K8S unit available.The file is almost self-explanatory: the definition gcr.io/google_containers/elasticsearch:v5.5.1- 1 indicates that a Docker Elasticsearch will be deployed. This image will have two replicas and uses persistent storage for persistent data.

There are many ways to deploy a tool. A Deployment, for example, is an upgrade from a replication controller that has mechanisms to perform rolling updates — updating a tool while keeping it available. Moreover, it is possible to configure Load Balancers, subnet, and even secrets through declarations.

K8s can be deployed in very different scenarios depending on the size of the company and its objectives:

• In-house: Organizations can transform their own data center into a K8s cluster. In this case, companies can take full advantage of their own resources.
• Cloud: The setup process is similar to an in-house deployment, but includes virtual machines on the cloud. This allows for the creation of a virtually infinite number of machines, depending on demand.
• Hybrid: An organization’s data center might perform well for most of the day, but sometimes a peak occurs that local computing resources cannot handle. In this case, a hybrid solution works well. When necessary, K8s will create virtual machines on the cloud to better distribute computing resources when on-premise servers are full.
• On-premise: Some cloud providers have their own K8s implementation embedded. In this case, there is no need to deploy and configure Kubernetes itself; an organization just needs to manage the service. Since deploying Kubernetes can be tricky, this is a good solution for companies that do not have a big IT team capable of handling cluster configuration and maintenance.
• Multicloud: This is the next level of a hybrid cloud solution. Computing resources are deployed among two or more cloud vendors. In this case, companies need to avoid vendor lock-in and minimize risk if something goes wrong.

Kubernetes Use Cases 

We have selected some common use cases to demonstrate Kubernetes’ capabilities to solve some challenges in real industries and it's overall impact.

1.AppDirect

Challenge

AppDirect provides an end-to-end commerce platform for cloud-based products and services. When Director of Software Development Pierre-Alexandre Lacerte began working there in 2014, the company had a monolith application deployed on a "tomcat infrastructure, and the whole release process was complex for what it should be," he says. "There were a lot of manual steps involved, with one engineer building a feature, then another team picking up the change. So you had bottlenecks in the pipeline to ship a feature to production." At the same time, the engineering team was growing, and the company realized it needed a better infrastructure to both support that growth and increase velocity.

Solution

"My idea was: Let's create an environment where teams can deploy their services faster, and they will say, 'Okay, I don't want to build in the monolith anymore. I want to build a service,'" says Lacerte. They considered and prototyped several different technologies before deciding to adopt Kubernetes in early 2016. Lacerte's team has also integrated Prometheus monitoring into the platform; tracing is next. Today, AppDirect has more than 50 microservices in production and 15 Kubernetes clusters deployed on AWS and on premise around the world.

Impact

The Kubernetes platform has helped support the engineering team's 10x growth over the past few years. Coupled with the fact that they were continually adding new features, Lacerte says, "I think our velocity would have slowed down a lot if we didn't have this new infrastructure." 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. "Onboarding a new service doesn't require Jira tickets or meeting with three different teams," says Lacerte. 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.

2.Pearson

Challenge

A global education company serving 75 million learners, Pearson set a goal to more than double that number, to 200 million, by 2025. A key part of this growth is in digital learning experiences, and Pearson was having difficulty in scaling and adapting to its growing online audience. They needed an infrastructure platform that would be able to scale quickly and deliver products to market faster.

Solution

"To transform our infrastructure, we had to think beyond simply enabling automated provisioning," says Chris Jackson, Director for Cloud Platforms & SRE at Pearson. "We realized we had to build a platform that would allow Pearson developers to build, manage and deploy applications in a completely different way." The team chose Docker container technology and Kubernetes orchestration "because of its flexibility, ease of management and the way it would improve our engineers' productivity."

Impact

With the platform, there has been substantial improvements in productivity and speed of delivery. "In some cases, we've gone from nine months to provision physical assets in a data center to just a few minutes to provision and get a new idea in front of a customer," says John Shirley, Lead Site Reliability Engineer for the Cloud Platform Team. Jackson estimates they've achieved 15-20% developer productivity savings. Before, outages were an issue during their busiest time of year, the back-to-school period. Now, there's high confidence in their ability to meet aggressive customer SLAs.

3.Nokia

Challenge

Nokia's core business is building telecom networks end-to-end; its main products are related to the infrastructure, such as antennas, switching equipment, and routing equipment. "As telecom vendors, we have to deliver our software to several telecom operators and put the software into their infrastructure, and each of the operators have a bit different infrastructure," says Gergely Csatari, Senior Open Source Engineer. "There are operators who are running on bare metal. There are operators who are running on virtual machines. There are operators who are running on VMware Cloud and OpenStack Cloud. We want to run the same product on all of these different infrastructures without changing the product itself."

Solution

The company decided that moving to cloud native technologies would allow teams to have infrastructure-agnostic behavior in their products. Teams at Nokia began experimenting with Kubernetes in pre-1.0 versions. "The simplicity of the label-based scheduling of Kubernetes was a sign that showed us this architecture will scale, will be stable, and will be good for our purposes," says Csatari. The first Kubernetes-based product, the Nokia Telephony Application Server, went live in early 2018. "Now, all the products are doing some kind of re-architecture work, and they're moving to Kubernetes."

Impact

Kubernetes has enabled Nokia's foray into 5G. "When you develop something that is part of the operator's infrastructure, you have to develop it for the future, and Kubernetes and containers are the forward-looking technologies," says Csatari. The teams using Kubernetes are already seeing clear benefits. "By separating the infrastructure and the application layer, we have less dependencies in the system, which means that it's easier to implement features in the application layer," says Csatari. And because teams can test the exact same binary artifact independently of the target execution environment, "we find more errors in early phases of the testing, and we do not need to run the same tests on different target environments, like VMware, OpenStack, or bare metal," he adds. As a result, "we save several hundred hours in every release."

3.IBM

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."

3.Babylon

Challenge

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

5.Huawei

Challenge

A multinational company that's the largest telecommunications equipment manufacturer in the world, Huawei has more than 180,000 employees. In order to support its fast business development around the globe, Huawei has eight data centers for its internal I.T. department, which have been running 800+ applications in 100K+ VMs to serve these 180,000 users. With the rapid increase of new applications, the cost and efficiency of management and deployment of VM-based apps all became critical challenges for business agility. "It's very much a distributed system so we found that managing all of the tasks in a more consistent way is always a challenge," says Peixin Hou, the company's Chief Software Architect and Community Director for Open Source. "We wanted to move into a more agile and decent practice."

Solution

After deciding to use container technology, Huawei began moving the internal I.T. department's applications to run on Kubernetes. So far, about 30 percent of these applications have been transferred to cloud native.

Impact

"By the end of 2016, Huawei's internal I.T. department managed more than 4,000 nodes with tens of thousands containers using a Kubernetes-based Platform as a Service (PaaS) solution," says Hou. "The global deployment cycles decreased from a week to minutes, and the efficiency of application delivery has been improved 10 fold." For the bottom line, he says, "We also see significant operating expense spending cut, in some circumstances 20-30 percent, which we think is very helpful for our business." Given the results Huawei has had internally – and the demand it is seeing externally – the company has also built the technologies into FusionStage?, the PaaS solution it offers its customers.

6.Pinterest

Challenge

After eight years in existence, Pinterest had grown into 1,000 microservices and multiple layers of infrastructure and diverse set-up tools and platforms. In 2016 the company launched a roadmap towards a new compute platform, led by the vision of creating the fastest path from an idea to production, without making engineers worry about the underlying infrastructure.

Solution

The first phase involved moving services to Docker containers. Once these services went into production in early 2017, the team began looking at orchestration to help create efficiencies and manage them in a decentralized way. After an evaluation of various solutions, Pinterest went with Kubernetes.

Impact

"By moving to Kubernetes the team was able to build on-demand scaling and new failover policies, in addition to simplifying the overall deployment and management of a complicated piece of infrastructure such as Jenkins," says Micheal Benedict, Product Manager for the Cloud and the Data Infrastructure Group at Pinterest. "We not only saw reduced build times but also huge efficiency wins. For instance, the team reclaimed over 80 percent of capacity during non-peak hours. As a result, the Jenkins Kubernetes cluster now uses 30 percent less instance-hours per-day when compared to the previous static cluster."

"So far it's been good, especially the elasticity around how we can configure our Jenkins workloads on that Kubernetes shared cluster. That is the win we were pushing for."

— MICHEAL BENEDICT, PRODUCT MANAGER FOR THE CLOUD AND THE DATA INFRASTRUCTURE GROUP AT PINTEREST

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