<commit_before>// Module included in the following assemblies:
//
// * architecture/architecture.adoc

[id='machine-api-overview-{context}']
= Machine API overview

For {product-title} 4 clusters, the Machine API performs all node
management actions after the cluster installation finishes. Because of this
system, {product-title} 4 offers an elastic, dynamic provisioning
method on top of public or private cloud infrastructure.

The Machine API is a combination of primary resources that are based on the upstream
link:https://github.com/kubernetes-sigs/cluster-api[Cluster API] project and
custom {product-title} resources.

The two primary resources are:

`Machines`:: A fundamental unit that describes a `Node`. A `machine` has a
class, which describes the types of compute nodes that are offered for different
cloud platforms. For example, a `machine` type for a worker node on Amazon Web
Services (AWS) might define a specific machine type and required metadata.
`MachineSets`:: Groups of machines. `MachineSets` are to `machines` as
`ReplicaSets` are to `Pods`. If you need more `machines` or need to scale them down,
you change the *replicas* field on the `MachineSet` to meet your compute need.


The following custom resources add more capabilities to your cluster:

`MachineAutoscaler`:: This resource automatically scales `machines` in
a cloud. You can set the minimum and maximum scaling boundaries for nodes in a
specified `MachineSet`, and the `MachineAutoscaler` 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`.
`MachineHealthCheck`:: This resource detects when a machine is unhealthy,
deletes it, and, on supported platforms, makes a new machine.
`ClusterAutoscaler`:: This resource is based on the upstream
link:https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler[ClusterAutoscaler]
project. In the {product-title} implementation, it is integrated with the
Cluster API by extending the `MachineSet` API.
////
`ClusterAutoscalerOperator`:: Instead of interacting with the `ClusterAutoscaler`
itself, you use its Operator. The `ClusterAutoscalerOperator` manages
the `ClusterAutoscaler` deployment. With this Operator, you can set cluster-wide
scaling limits for resources such as cores, nodes, memory, and GPU.
and so on. You can set the priority so that the cluster prioritizes pods so that
new nodes are not brought online for less important pods. You can also set the
ScalingPolicy so you can scale up nodes but not scale them down.
////


In {product-title} version 3.11, you could not roll out a multi-zone architecture easily because the cluster
did not manage machine provisioning. It is easier in 4.0. Each `MachineSet` is scoped
to a single zone, so the installation program sends out `MachineSets` 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 need to rebalance
your machines. The autoscaler provides best-effort balancing over the life of a cluster.<commit_msg>updating ClusterAutoscaler<commit_after>// Module included in the following assemblies:
//
// * architecture/architecture.adoc

[id='machine-api-overview-{context}']
= Machine API overview

For {product-title} 4 clusters, the Machine API performs all node
management actions after the cluster installation finishes. Because of this
system, {product-title} 4 offers an elastic, dynamic provisioning
method on top of public or private cloud infrastructure.

The Machine API is a combination of primary resources that are based on the upstream
link:https://github.com/kubernetes-sigs/cluster-api[Cluster API] project and
custom {product-title} resources.

The two primary resources are:

`Machines`:: A fundamental unit that describes a `Node`. A `machine` has a
class, which describes the types of compute nodes that are offered for different
cloud platforms. For example, a `machine` type for a worker node on Amazon Web
Services (AWS) might define a specific machine type and required metadata.
`MachineSets`:: Groups of machines. `MachineSets` are to `machines` as
`ReplicaSets` are to `Pods`. If you need more `machines` or need to scale them down,
you change the *replicas* field on the `MachineSet` to meet your compute need.


The following custom resources add more capabilities to your cluster:

`MachineAutoscaler`:: This resource automatically scales `machines` in
a cloud. You can set the minimum and maximum scaling boundaries for nodes in a
specified `MachineSet`, and the `MachineAutoscaler` 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`.
`MachineHealthCheck`:: This resource detects when a machine is unhealthy,
deletes it, and, on supported platforms, makes a new machine.
`ClusterAutoscaler`:: This resource is based on the upstream
link:https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler[ClusterAutoscaler]
project. In the {product-title} implementation, it is integrated with the
Cluster API by extending the `MachineSet` API. You can set cluster-wide
scaling limits for resources such as cores, nodes, memory, GPU,
and so on. You can set the priority so that the cluster prioritizes pods so that
new nodes are not brought online for less important pods. You can also set the
ScalingPolicy so you can scale up nodes but not scale them down.
////
`ClusterAutoscalerOperator`:: Instead of interacting with the `ClusterAutoscaler`
itself, you use its Operator. The `ClusterAutoscalerOperator` manages
the `ClusterAutoscaler` deployment.
////


In {product-title} version 3.11, you could not roll out a multi-zone architecture easily because the cluster
did not manage machine provisioning. It is easier in 4.0. Each `MachineSet` is scoped
to a single zone, so the installation program sends out `MachineSets` 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 need to rebalance
your machines. The autoscaler provides best-effort balancing over the life of a cluster.
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