You can use machine management to flexibly work with underlying infrastructure such as Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), Red Hat OpenStack Platform (RHOSP), and VMware vSphere to manage the OpenShift Container Platform cluster. You can control the cluster and perform auto-scaling, such as scaling up and down the cluster based on specific workload policies.
It is important to have a cluster that adapts to changing workloads. The OpenShift Container Platform cluster can horizontally scale up and down when the load increases or decreases.
Machine management is implemented as a custom resource definition (CRD).
A CRD object defines a new unique object Kind
in the cluster and enables the Kubernetes API server to handle the object’s entire lifecycle.
The Machine API Operator provisions the following resources:
MachineSet
Machine
ClusterAutoscaler
MachineAutoscaler
MachineHealthCheck
The Machine API is a combination of primary resources that are based on the upstream Cluster API project and custom OpenShift Container Platform resources.
For OpenShift Container Platform 4.15 clusters, the Machine API performs all node host provisioning management actions after the cluster installation finishes. Because of this system, OpenShift Container Platform 4.15 offers an elastic, dynamic provisioning method on top of public or private cloud infrastructure.
The two primary resources are:
A fundamental unit that describes the host for a node. A machine has a providerSpec
specification, which describes the types of compute nodes that are offered for different cloud platforms. For example, a machine type for a compute node might define a specific machine type and required metadata.
MachineSet
resources are groups of compute machines. Compute machine sets are to compute machines as replica sets are to pods. If you need more compute machines or must scale them down, you change the replicas
field on the MachineSet
resource to meet your compute need.
Control plane machines cannot be managed by compute machine sets. Control plane machine sets provide management capabilities for supported control plane machines that are similar to what compute machine sets provide for compute machines. For more information, see “Managing control plane machines". |
The following custom resources add more capabilities to your cluster:
The MachineAutoscaler
resource automatically scales compute machines in a cloud. You can set the minimum and maximum scaling boundaries for nodes in a specified compute machine set, and the machine autoscaler maintains that range of nodes.
The MachineAutoscaler
object takes effect after a ClusterAutoscaler
object exists. Both ClusterAutoscaler
and MachineAutoscaler
resources are made available by the ClusterAutoscalerOperator
object.
This resource is based on the upstream cluster autoscaler project. In the OpenShift Container Platform implementation, it is integrated with the Machine API by extending the compute machine set API. You can use the cluster autoscaler to manage your cluster in the following ways:
Set cluster-wide scaling limits for resources such as cores, nodes, memory, and GPU
Set the priority so that the cluster prioritizes pods and new nodes are not brought online for less important pods
Set the scaling policy so that you can scale up nodes but not scale them down
The MachineHealthCheck
resource detects when a machine is unhealthy, deletes it, and, on supported platforms, makes a new machine.
In OpenShift Container Platform version 3.11, you could not roll out a multi-zone architecture easily because the cluster did not manage machine provisioning. Beginning with OpenShift Container Platform version 4.1, this process is easier. Each compute machine set is scoped to a single zone, so the installation program sends out compute machine sets across availability zones on your behalf. And then because your compute is dynamic, and in the face of a zone failure, you always have a zone for when you must rebalance your machines. In global Azure regions that do not have multiple availability zones, you can use availability sets to ensure high availability. The autoscaler provides best-effort balancing over the life of a cluster.
As a cluster administrator, you can perform the following actions:
Create a compute machine set for the following cloud providers:
Create a machine set for a bare metal deployment: Creating a compute machine set on bare metal
Manually scale a compute machine set by adding or removing a machine from the compute machine set.
Modify a compute machine set through the MachineSet
YAML configuration file.
Delete a machine.
Configure and deploy a machine health check to automatically fix damaged machines in a machine pool.
As a cluster administrator, you can perform the following actions:
Update your control plane configuration with a control plane machine set for the following cloud providers:
Configure and deploy a machine health check to automatically recover unhealthy control plane machines.
You can automatically scale your OpenShift Container Platform cluster to ensure flexibility for changing workloads. To autoscale your cluster, you must first deploy a cluster autoscaler, and then deploy a machine autoscaler for each compute machine set.
The cluster autoscaler increases and decreases the size of the cluster based on deployment needs.
The machine autoscaler adjusts the number of machines in the compute machine sets that you deploy in your OpenShift Container Platform cluster.
User-provisioned infrastructure is an environment where you can deploy infrastructure such as compute, network, and storage resources that host the OpenShift Container Platform. You can add compute machines to a cluster on user-provisioned infrastructure during or after the installation process.
As a cluster administrator, you can perform the following actions:
Add Red Hat Enterprise Linux (RHEL) compute machines, also known as worker machines, to a user-provisioned infrastructure cluster or an installation-provisioned infrastructure cluster.
Add more Red Hat Enterprise Linux (RHEL) compute machines to an existing cluster.