- Documentation
- OpenShift Dedicated
- Cluster administration
- Nodes
- About machine pools history bug_report picture_as_pdf
- About
- What's new
- Introduction to OpenShift Dedicated
- Architecture
- Red Hat OpenShift Cluster Manager
- Planning your environment
- Getting started
- Installing, accessing, and deleting OpenShift Dedicated clusters
- Creating a cluster on AWS
- Creating a GCP Private Service Connect enabled private cluster
- Creating a cluster on GCP with Workload Identity Federation
- Creating a cluster on GCP
- Configuring your identity providers
- Revoking privileges and access to an OpenShift Dedicated cluster
- Deleting an OpenShift Dedicated cluster
- Support
- Web console
- CLI tools
- Cluster administration
- Security and compliance
- Authentication and authorization
- Authentication and authorization overview
- Understanding authentication
- Managing user-owned OAuth access tokens
- Configuring identity providers
- Managing administration roles and users
- Using RBAC to define and apply permissions
- Understanding and creating service accounts
- Using service accounts in applications
- Using a service account as an OAuth client
- Scoping tokens
- Using bound service account tokens
- Managing security context constraints
- Understanding and managing pod security admission
- Syncing LDAP groups
- Upgrading
- CI/CD
- Images
- Overview of images
- Overview of the Cluster Samples Operator
- Using the Cluster Samples Operator with an alternate registry
- Creating images
- Managing images
- Managing image streams
- Using image streams with Kubernetes resources
- Triggering updates on image stream changes
- Image configuration resources
- Using templates
- Using Ruby on Rails
- Using images
- Add-on services
- Storage
- Registry
- Operators
- Operators overview
- Understanding Operators
- User tasks
- Administrator tasks
- Developing Operators
- About the Operator SDK
- Installing the Operator SDK CLI
- Go-based Operators
- Ansible-based Operators
- Helm-based Operators
- Defining cluster service versions (CSVs)
- Working with bundle images
- Complying with pod security admission
- Validating Operators using the scorecard
- Validating Operator bundles
- High-availability or single-node cluster detection and support
- Configuring built-in monitoring with Prometheus
- Configuring leader election
- Object pruning utility
- Migrating package manifest projects to bundle format
- Operator SDK CLI reference
- Migrating to Operator SDK v0.1.0
- Networking
- Building applications
- Building applications overview
- Projects
- Creating applications
- Viewing application composition using the Topology view
- Working with Helm charts
- Deployments
- Quotas
- Using config maps with applications
- Monitoring project and application metrics using the Developer perspective
- Monitoring application health
- Editing applications
- Working with quotas
- Pruning objects to reclaim resources
- Idling applications
- Deleting applications
- Using the Red Hat Marketplace
- Nodes
- Overview of nodes
- Working with pods
- Automatically scaling pods with the Custom Metrics Autoscaler Operator
- Release notes
- Custom Metrics Autoscaler Operator overview
- Installing the custom metrics autoscaler
- Understanding the custom metrics autoscaler triggers
- Understanding the custom metrics autoscaler trigger authentications
- Pausing the custom metrics autoscaler
- Gathering audit logs
- Gathering debugging data
- Viewing Operator metrics
- Understanding how to add custom metrics autoscalers
- Removing the Custom Metrics Autoscaler Operator
- Controlling pod placement onto nodes (scheduling)
- About pod placement using the scheduler
- Placing pods relative to other pods using pod affinity and anti-affinity rules
- Controlling pod placement on nodes using node affinity rules
- Placing pods onto overcommited nodes
- Placing pods on specific nodes using node selectors
- Controlling pod placement using pod topology spread constraints
- Using jobs and daemon sets
- Working with nodes
- Working with containers
- Understanding containers
- Using Init Containers to perform tasks before a pod is deployed
- Using volumes to persist container data
- Mapping volumes using projected volumes
- Allowing containers to consume API objects
- Copying files to or from a container
- Executing remote commands in a container
- Using port forwarding to access applications in a container
- Working with clusters
- Observability
- Observability overview
- Monitoring
- Monitoring overview
- Accessing monitoring for user-defined projects
- Configuring the monitoring stack
- Disabling monitoring for user-defined projects
- Enabling alert routing for user-defined projects
- Managing metrics
- Managing alerts
- Reviewing monitoring dashboards
- Accessing third-party monitoring APIs
- Troubleshooting monitoring issues
- Config map reference for the Cluster Monitoring Operator
- Service Mesh
- Service Mesh 2.x
- About OpenShift Service Mesh
- Service Mesh 2.x release notes
- Service Mesh architecture
- Service Mesh deployment models
- Service Mesh and Istio differences
- Preparing to install Service Mesh
- Installing the Operators
- Creating the ServiceMeshControlPlane
- Adding workloads to a service mesh
- Enabling sidecar injection
- Upgrading Service Mesh
- Managing users and profiles
- Security
- Traffic management
- Metrics, logs, and traces
- Performance and scalability
- Deploying to production
- Federation
- Extensions
- 3scale WebAssembly for 2.1
- 3scale Istio adapter for 2.0
- Troubleshooting Service Mesh
- Control plane configuration reference
- Kiali configuration reference
- Jaeger configuration reference
- Uninstalling Service Mesh
- Service Mesh 2.x
- Serverless
About machine pools
OpenShift Dedicated uses machine pools as an elastic, dynamic provisioning method on top of your cloud infrastructure.
The primary resources are machines, machine sets, and machine pools.
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As of OpenShift Dedicated 4.11, the default per-pod PID limit is You cannot configure the per-pod PID limit on any OpenShift Dedicated cluster. |
Machine sets
MachineSet resources are groups of compute machines. If you need more machines or must scale them down, change the number of replicas in the machine pool to which the compute machine sets belong.
Machine pools
Machine pools are a higher level construct to compute machine sets.
A machine pool creates compute machine sets that are all clones of the same configuration across availability zones. Machine pools perform all of the host node provisioning management actions on a worker node. If you need more machines or must scale them down, change the number of replicas in the machine pool to meet your compute needs. You can manually configure scaling or set autoscaling.
By default, a cluster is created with one machine pool. You can add additional machine pools to an existing cluster, modify the default machine pool, and delete machine pools.
Multiple machine pools can exist on a single cluster, and they can each have different types or different size nodes.
Machine pools in multiple zone clusters
When you create a machine pool in a multiple availability zone (Multi-AZ) cluster, that one machine pool has 3 zones. The machine pool, in turn, creates a total of 3 compute machine sets - one for each zone in the cluster. Each of those compute machine sets manages one or more machines in its respective availability zone.
If you create a new Multi-AZ cluster, the machine pools are replicated to those zones automatically. If you add a machine pool to an existing Multi-AZ, the new pool is automatically created in those zones. Similarly, deleting a machine pool will delete it from all zones. Due to this multiplicative effect, using machine pools in Multi-AZ cluster can consume more of your project’s quota for a specific region when creating machine pools.