![]() ![]() Service account and token controllers: – Responsible for creating default accounts and the API access token.Endpoints controller: – it creates the Endpoints object, for example, to expose the pod externally we need to join it to a service.Replication controller: – It ensures that the correct numbers of pods are running for every replication controller object in the cluster.Node controller: – it manages nodes and keeps an eye on available nodes in the cluster and responds if any node goes down.It includes node controller, replication controller, endpoints controller, and service account and token controllers. It ensures that the current state matches the desired state, if the current state does not match the desired state, it makes appropriate changes to the cluster to achieve the desired state. It is actually comprised of four processes and runs as a single process to reduce complexity. Kube-controller-manager is responsible for running controller processes. If more than one node has the same score then it chooses one randomly. Once it filters out all feasible nodes, it assigns a score to each feasible node based on active score rules and it runs the pod on the node which has the highest score. In filtering, Kube-scheduler finds a feasible node by running checks like node has enough available resource that is mentioned for this pod. Kube-scheduler uses 2 step process to select a node for the pod in the cluster, filtering, and scoring. Feasible node is the node that fulfills all the requirements for a pod to schedule. If there is no node available that meets the specified requirements then the pod is not deployed and it remains unscheduled until the Kube-scheduler does not find a feasible node. by specifying affinity, anti-specification or constraint in the YAML file before deploying a pod or a deployment. However, it is possible to schedule a pod or a group of pods on a specific node, in a specific zone or as per node label, etc. ![]() Kube-scheduler is responsible for scheduling newly created pods to the best available nodes to run in the cluster. There is more than one api-server that can be deployed horizontally to balance the traffic using a load balancer. It services REST operations and provides a front end for the Kubernetes control plane that exposes the Kubernetes API through which other components can communicate to the cluster. Kube-api-server is the main component of the control plane as all traffic goes through api-server, other components of the control plane also connect to api-server if they have to communicate with ‘etcd’ datastore as only Kube-api-server can communicate with ‘etcd’. Kubernetes Control Plane has five components as below: Let’s understand about different components of Kubernetes Control Plane. ‘kube-apiserver’ communicate with other control plane’s component that is ‘etcd’ data store and it fetches the data and sends back to the console via HTTPs and we see the details of nodes on our terminal. Here, when we run this command, it makes an API call through HTTPs to the cluster and it is handled by ‘kube-apiserver’. $ kubectl get nodes: The kubectl is a command-line tool that we use to interact with the Kubernetes cluster and manage it. Let’s understand the working of Kubernetes control plane by an example, given below: – Kubernetes now has good support for clusters with several control planes.Web development, programming languages, Software testing & others You should consider manually setting up replication of the etcd cluster so it doesn’t become a separate point of failure. It relies on an external etcd instance which will be shared by your control plane nodes. – This approach is similar to the stacked model but with one key difference. You ideally need an odd number of hosts, such as 3, 5, or 7, to optimize the election process. If the leader goes offline, the other nodes will notice its absence and a new leader will be elected. One host will assume responsibility for the cluster by being designated as the leader. ![]() Each machine will run its own control plane that replicates data from the others. – This approach requires less infrastructure and works with a minimum of three machines. ![]()
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