With the increase in the container adoption rate by the organization, the container number is increasing at a faster pace. The container is rising in popularity because it has revolutionized the way the software is built and deployed. This has further generated the need for a container orchestration tool that automates the maintenance, deployment, and scaling of the containers. This is where Kubernetes proves extremely beneficial for businesses. It is an open-source system first created by Google and then released as an open-source project that is used for managing containerized services like declarative setup and automation it also manages the scalability of the application and facilitates deployment functionalities.

Developers get a framework to manage workloads, provide declarative configuration, and build distributed systems with Kubernetes. Made with a set of core components, it can be either used separately or together to manage containers across different hosts. It facilitates control groups to manage the resources of containers and also helps the system stay stable by bringing scalability and reliability. Kubernetes also ensures that containers run in a healthy state and it gracefully handles node failures. Its auto-scaling feature eases the cluster creation process for developers and allows the cluster to be created without any help from humans.

Nodes are worker machines running containerized programs and hosting the Pods that make up the application workload. Kubernetes nodes make a Kubernetes cluster and every cluster has a minimum of one worker node. Cluster workers’ nodes and Pods are managed by the control plane that is usually distributed across many computers. The cluster runs multiple nodes, so provides high availability and fault tolerance. Kubernetes with high availability is a system that minimizes the downtime or the time when the service is unavailable to a user and has an extremely low chance of being affected. Kubernetes high availability is a set of configurations that offers a minimal level of service, like if a node goes down then also the application will provide a minimal level of service. Kubernetes with high availability can be achieved in several ways such as failover, replication, etc.

For the high availability of Kubernetes, many container clones in Kubernetes are run that are similar to virtual machines. The controller manager and API server are vital components that are replicated on several masters in Kubernetes high availability system, so even if one master fails, the remaining masters keep the cluster functioning. Thus it ensures that Kubernetes and its supporting components do not have any single point of failure. One maser cluster can be prone to failure, but a multi-master cluster makes use of several master nodes where each node has access to the same worker nodes. Thus, organizations make use of additional master nodes to make sure high availability and optimize every cluster performance.

For implementing Kubernete’s high availability, businesses have to find out the expected level of availability in their application, as the desired downtime level varies according to the application and the business objectives. Businesses should deploy an application using a redundant control plane that makes sure that applications are available to users. Lastly, businesses should replicate the data in every node in the cluster by deploying the application with a redundant data plane. Kubernete’s high availability comes in active-active and active-passive clusters.

Kubernete’s high availability in active-active cluster form is provided by running multiple copies of services across all the nodes in the cluster and its availability in active-passive clusters is provided by making use of backup nodes that are usually idle, as only one copy of the service run at any given time but if required they can be quickly enhanced to handle the load. A highly available cluster can be created by using the Kubernetes control plane that schedules containers for resources. Kubernetes use replication controllers to prevent single points of failure and hence the failure of any hardware or software component will not affect the entire cluster.