Kubernetes in simple words is an open-source platform that automates Linux container operations. It eliminates many of the manual processes involved in deploying and scaling containerized applications. In other words, we can cluster together groups of hosts running Linux containers, and Kubernetes helps us easily and efficiently manage those clusters.
Kubernetes eases the burden of configuring, deploying, managing, and monitoring even the largest-scale containerized applications.
Some of the features of Kubernetes:-
- Automated Scheduling.
- Self-Healing Capabilities.
- Automated rollouts & rollback.
- Horizontal Scaling & Load Balancing.
- Offers environment consistency for development, testing, and production.
2253 companies reportedly use Kubernetes in their tech stacks, including Google, Shopify, and Slack.
Some of the Use Cases solved by Kubernetes in Industries:-
Challenges faced :
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.
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. They use Kubernetes mainly as a batch scheduling system and rely on our autoscaler to dynamically scale up and down our cluster. This significantly reduces costs for idle nodes, while still providing low latency and rapid iteration.
The company has benefited from greater portability because Kubernetes provides a consistent API which helps in moving their research experiments very easily between clusters.
They are able to use their own data centers thus lowering costs and providing them access to hardware that they wouldn’t necessarily have access to in the cloud. Launching experiments also takes far less time.
2. The New York Times
The company had decided a few years ago to move out of its data centers, its first deployments on the public cloud were smaller, less critical applications managed on virtual machines. They started building more and more tools, and at some point, they realized that they were doing a disservice by treating Amazon as another data center. Kapadia was tapped to lead a Delivery Engineering Team that would design for the abstractions that cloud providers offer them.
Speed of delivery increased. Some of the legacies VM-based deployments that took 45 minutes; with Kubernetes, that time was reduced to just a few seconds to a couple of minutes. Teams that used to deploy on weekly schedules or had to coordinate schedules with the infrastructure team now deploy their updates independently, and can do it daily when necessary.” Adopting Cloud Native Computing Foundation technologies allowed for a more unified approach to deployment across the engineering staff, and portability for the company.