Theory

In today’s digital world, scalable applications that run efficiently are important and needed for Big Tech and other companies like Google, Amazon etc that provide services to billions and billions of people around the world. Managing these applications manually across multiple servers can be complex and time-consuming. Kubernetes simplifies this process by automating application deployment, scaling, and management.

Kubernetes runs on a cluster model, with a Control Plane as the Master Node and several worker nodes under it. The Master Node is responsible for monitoring, while worker nodes run workloads within Pods that contain one or more containers. Kubernetes provides high availability across billions of users, solves the problem for load balancing and has backup nodes incase of redistribution or failures.

For example A global e-commerce platform uses Kubernetes to scale up during peak sales and scale down during off-peak hours, optimizing costs. In AI applications, Kubernetes efficiently distributes deep learning workloads across multiple GPUs, ensuring optimal resource utilization without manual intervention.