The future of IT organizations revolves around seamless integrated experiences, machine learning-based decision-making, predictive analytics, high interactivity, and artificial intelligence. Kubernetes helps organizations achieve these for orchestrating multiple containers among different server clusters. According to a recent survey, 60% organizations are running nearly half of the containerized applications on Kubernetes.

Gartner predicts over 75% organizations worldwide to run their containerized applications in production by 2022. With increasing inclination toward containerization, demand for container management continues to grow. This has further influenced the adoption of Kubernetes, as organizations seek seamless support for managing their containers in production environments.

Key Kubernetes Deployment Strategies 

When it comes to Kubernetes deployment, there are some unique ways of releasing applications and choosing the right strategy is imperative. Some of the key strategies that can be followed by organizations include A/B testing, ramped, canary, recreate, and blue/green.

  • A/B Testing: This strategy is feasible with regards to feature testing for a subdivision of users. It helps businesses make decisions driven more by statistics rather than deployment strategies. Along with distributing traffic, A/B testing enables organizations to target users precisely on the basis of key parameters, such as user agent. Widely used for testing conversion of features, it helps organizations to roll out the version of applications that bring maximum conversions.
  • Ramped: This kind of Kubernetes deployment helps organizations update pods in the rolling format. Here, an additional replica of an application is created along with its new version. After doing so, the replicas of older versions of the application are ebbed and the newer versions are ramped up until a satisfactory number of replicas are created. This helps organizations to gradually release the versions of applications across instances. It provides convenience in the case of stateful applications and manages data rebalancing.
  • Canary: Canary helps organizations to do the testing by themselves by routing a subdivision of users to newer functionalities. Canary deployment allows replicas of new versions to be released abreast an old version. After enough duration where errors are eliminated, organizations can scale up the replicas of the new version while discarding the old deployment.
  • Recreate: Recreate is the best deployment strategy for the development environment. This strategy is designed to eliminate every running instance and recreate them along with newer application versions. It helps organizations to completely renew the state of the application.
  • Blue/Green: In the blue/green deployment, organizations are capable of updating objects that act as load balancers for sending traffic to new versions. This is done only after testing whether the new version of application fulfils said requirements. It further enables organizations to realize instant rollout and avoid versioning issues faced with applications.

While there are many ways for application deployment, ramped deployment or recreate is touted to be the best fit for most organizations. However, precise testing of new applications and platforms is imperative to prevent any impact on releasing the new version of application.

Kubernetes Control Plane Components

Control plane is dedicated to maintaining an object’s desired state in a cluster. The control plane components are run on single nodes or multiple nodes as required in the production for achieving fault tolerance and high availability. Every control plane component is placed with unique responsibility while together they enable organizations to make decisions regarding clusters.

Key Components Include

  • Cloud-Controller-Manager: It allows organizations to connect on-premises clusters to cloud-hosted clusters. Controllers of this component rely on the cloud platform used for running workloads. Cloud-controller-manager involves three controllers, namely, service, route, and node, in single procedure. Node controller helps tracking the status of the cloud-hosted nodes. Route controller helps setting up and controlling the route in the cloud infrastructure. Service controller enables to create, delete, and update the load balancers.
  • Kube-Controller-Manager: It helps in running the processes of controllers and involves four processes to match the current state to the desired state of the cluster. These processes are node, endpoints, service account, and replication. Service account helps create API access tokens and default accounts. The endpoints controller enables development of endpoints objects. While replication ensures the right count of pods for each controller object, node controller manages and monitors the nodes available in the cluster.
  • Kube-API-Server: This is the most important control plane component, connecting all the other components for communication with the etcd data storage. The REST operations are serviced by Kube-API-server and multiple api-servers can be horizontally deployed for balancing the traffic.
  • Etcd: This stores all the data of the cluster and ensures a highly available, distributed, and consistent storage. This storage is accessible only by the Kube-API-server. Etcd is completely different from the view of an open-source product backend and requires setting up the right backup plan for faster data recovery in the case of incidents and disasters.
  • Kube-Scheduler: It is responsible to schedule new pods created for the available nodes and for running in the clusters. In case there are no nodes available to meet said requirements, it enables the pods to remain unscheduled until a viable node is identified by the Kube-scheduler.

To Conclude

Leveraging Kubernetes is one of the key emphasis areas for organizations, however, running the technology and rolling out applications by themselves will bring challenges. The right technology provider with pre-made and centrally orchestrated Kubernetes cloud can help organizations to completely scale and support their technology stack across multiple cloud environments. 

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