$ oc extract -n openshift-machine-api secret/worker-user-data-managed --keys=userData --to=- > worker.ign
After installing OKD, you can further expand and customize your cluster to your requirements through certain node tasks.
You can add more Fedora CoreOS (FCOS) compute machines to your OKD cluster on bare metal.
Before you add more compute machines to a cluster that you installed on bare metal infrastructure, you must create FCOS machines for it to use. You can either use an ISO image or network PXE booting to create the machines.
You installed a cluster on bare metal.
You have installation media and Fedora CoreOS (FCOS) images that you used to create your cluster. If you do not have these files, you must obtain them by following the instructions in the installation procedure.
You can create more Fedora CoreOS (FCOS) compute machines for your bare metal cluster by using an ISO image to create the machines.
Obtain the URL of the Ignition config file for the compute machines for your cluster. You uploaded this file to your HTTP server during installation.
You must have the OpenShift CLI (oc
) installed.
Extract the Ignition config file from the cluster by running the following command:
$ oc extract -n openshift-machine-api secret/worker-user-data-managed --keys=userData --to=- > worker.ign
Upload the worker.ign
Ignition config file you exported from your cluster to your HTTP server. Note the URLs of these files.
You can validate that the ignition files are available on the URLs. The following example gets the Ignition config files for the compute node:
$ curl -k http://<HTTP_server>/worker.ign
You can access the ISO image for booting your new machine by running to following command:
RHCOS_VHD_ORIGIN_URL=$(oc -n openshift-machine-config-operator get configmap/coreos-bootimages -o jsonpath='{.data.stream}' | jq -r '.architectures.<architecture>.artifacts.metal.formats.iso.disk.location')
Use the ISO file to install FCOS on more compute machines. Use the same method that you used when you created machines before you installed the cluster:
Burn the ISO image to a disk and boot it directly.
Use ISO redirection with a LOM interface.
Boot the FCOS ISO image without specifying any options, or interrupting the live boot sequence. Wait for the installer to boot into a shell prompt in the FCOS live environment.
You can interrupt the FCOS installation boot process to add kernel arguments. However, for this ISO procedure you must use the |
Run the coreos-installer
command and specify the options that meet your installation requirements. At a minimum, you must specify the URL that points to the Ignition config file for the node type, and the device that you are installing to:
$ sudo coreos-installer install --ignition-url=http://<HTTP_server>/<node_type>.ign <device> --ignition-hash=sha512-<digest> (1) (2)
1 | You must run the coreos-installer command by using sudo , because the core user does not have the required root privileges to perform the installation. |
2 | The --ignition-hash option is required when the Ignition config file is obtained through an HTTP URL to validate the authenticity of the Ignition config file on the cluster node. <digest> is the Ignition config file SHA512 digest obtained in a preceding step. |
If you want to provide your Ignition config files through an HTTPS server that uses TLS, you can add the internal certificate authority (CA) to the system trust store before running |
The following example initializes a bootstrap node installation to the /dev/sda
device. The Ignition config file for the bootstrap node is obtained from an HTTP web server with the IP address 192.168.1.2:
$ sudo coreos-installer install --ignition-url=http://192.168.1.2:80/installation_directory/bootstrap.ign /dev/sda --ignition-hash=sha512-a5a2d43879223273c9b60af66b44202a1d1248fc01cf156c46d4a79f552b6bad47bc8cc78ddf0116e80c59d2ea9e32ba53bc807afbca581aa059311def2c3e3b
Monitor the progress of the FCOS installation on the console of the machine.
Ensure that the installation is successful on each node before commencing with the OKD installation. Observing the installation process can also help to determine the cause of FCOS installation issues that might arise. |
Continue to create more compute machines for your cluster.
You can create more Fedora CoreOS (FCOS) compute machines for your bare metal cluster by using PXE or iPXE booting.
Obtain the URL of the Ignition config file for the compute machines for your cluster. You uploaded this file to your HTTP server during installation.
Obtain the URLs of the FCOS ISO image, compressed metal BIOS, kernel
, and initramfs
files that you uploaded to your HTTP server during cluster installation.
You have access to the PXE booting infrastructure that you used to create the machines for your OKD cluster during installation. The machines must boot from their local disks after FCOS is installed on them.
If you use UEFI, you have access to the grub.conf
file that you modified during OKD installation.
Confirm that your PXE or iPXE installation for the FCOS images is correct.
For PXE:
DEFAULT pxeboot TIMEOUT 20 PROMPT 0 LABEL pxeboot KERNEL http://<HTTP_server>/rhcos-<version>-live-kernel-<architecture> (1) APPEND initrd=http://<HTTP_server>/rhcos-<version>-live-initramfs.<architecture>.img coreos.inst.install_dev=/dev/sda coreos.inst.ignition_url=http://<HTTP_server>/worker.ign coreos.live.rootfs_url=http://<HTTP_server>/rhcos-<version>-live-rootfs.<architecture>.img (2)
1 | Specify the location of the live kernel file that you uploaded to your HTTP server. |
2 | Specify locations of the FCOS files that you uploaded to your HTTP server. The initrd parameter value is the location of the live initramfs file, the coreos.inst.ignition_url parameter value is the location of the worker Ignition config file, and the coreos.live.rootfs_url parameter value is the location of the live rootfs file. The coreos.inst.ignition_url and coreos.live.rootfs_url parameters only support HTTP and HTTPS. |
This configuration does not enable serial console access on machines with a graphical console. To configure a different console, add one or more |
For iPXE (x86_64
+ aarch64
):
kernel http://<HTTP_server>/rhcos-<version>-live-kernel-<architecture> initrd=main coreos.live.rootfs_url=http://<HTTP_server>/rhcos-<version>-live-rootfs.<architecture>.img coreos.inst.install_dev=/dev/sda coreos.inst.ignition_url=http://<HTTP_server>/worker.ign (1) (2) initrd --name main http://<HTTP_server>/rhcos-<version>-live-initramfs.<architecture>.img (3) boot
1 | Specify the locations of the FCOS files that you uploaded to your
HTTP server. The kernel parameter value is the location of the kernel file,
the initrd=main argument is needed for booting on UEFI systems,
the coreos.live.rootfs_url parameter value is the location of the rootfs file,
and the coreos.inst.ignition_url parameter value is the
location of the worker Ignition config file. |
2 | If you use multiple NICs, specify a single interface in the ip option.
For example, to use DHCP on a NIC that is named eno1 , set ip=eno1:dhcp . |
3 | Specify the location of the initramfs file that you uploaded to your HTTP server. |
This configuration does not enable serial console access on machines with a graphical console To configure a different console, add one or more |
To network boot the CoreOS |
For PXE (with UEFI and GRUB as second stage) on aarch64
:
menuentry 'Install CoreOS' { linux rhcos-<version>-live-kernel-<architecture> coreos.live.rootfs_url=http://<HTTP_server>/rhcos-<version>-live-rootfs.<architecture>.img coreos.inst.install_dev=/dev/sda coreos.inst.ignition_url=http://<HTTP_server>/worker.ign (1) (2) initrd rhcos-<version>-live-initramfs.<architecture>.img (3) }
1 | Specify the locations of the FCOS files that you uploaded to your
HTTP/TFTP server. The kernel parameter value is the location of the kernel file on your TFTP server.
The coreos.live.rootfs_url parameter value is the location of the rootfs file, and the coreos.inst.ignition_url parameter value is the location of the worker Ignition config file on your HTTP Server. |
2 | If you use multiple NICs, specify a single interface in the ip option.
For example, to use DHCP on a NIC that is named eno1 , set ip=eno1:dhcp . |
3 | Specify the location of the initramfs file that you uploaded to your TFTP server. |
Use the PXE or iPXE infrastructure to create the required compute machines for your cluster.
When you add machines to a cluster, two pending certificate signing requests (CSRs) are generated for each machine that you added. You must confirm that these CSRs are approved or, if necessary, approve them yourself. The client requests must be approved first, followed by the server requests.
You added machines to your cluster.
Confirm that the cluster recognizes the machines:
$ oc get nodes
NAME STATUS ROLES AGE VERSION
master-0 Ready master 63m v1.26.0
master-1 Ready master 63m v1.26.0
master-2 Ready master 64m v1.26.0
The output lists all of the machines that you created.
The preceding output might not include the compute nodes, also known as worker nodes, until some CSRs are approved. |
Review the pending CSRs and ensure that you see the client requests with the Pending
or Approved
status for each machine that you added to the cluster:
$ oc get csr
NAME AGE REQUESTOR CONDITION
csr-8b2br 15m system:serviceaccount:openshift-machine-config-operator:node-bootstrapper Pending
csr-8vnps 15m system:serviceaccount:openshift-machine-config-operator:node-bootstrapper Pending
...
In this example, two machines are joining the cluster. You might see more approved CSRs in the list.
If the CSRs were not approved, after all of the pending CSRs for the machines you added are in Pending
status, approve the CSRs for your cluster machines:
Because the CSRs rotate automatically, approve your CSRs within an hour of adding the machines to the cluster. If you do not approve them within an hour, the certificates will rotate, and more than two certificates will be present for each node. You must approve all of these certificates. After the client CSR is approved, the Kubelet creates a secondary CSR for the serving certificate, which requires manual approval. Then, subsequent serving certificate renewal requests are automatically approved by the |
For clusters running on platforms that are not machine API enabled, such as bare metal and other user-provisioned infrastructure, you must implement a method of automatically approving the kubelet serving certificate requests (CSRs). If a request is not approved, then the |
To approve them individually, run the following command for each valid CSR:
$ oc adm certificate approve <csr_name> (1)
1 | <csr_name> is the name of a CSR from the list of current CSRs. |
To approve all pending CSRs, run the following command:
$ oc get csr -o go-template='{{range .items}}{{if not .status}}{{.metadata.name}}{{"\n"}}{{end}}{{end}}' | xargs --no-run-if-empty oc adm certificate approve
Some Operators might not become available until some CSRs are approved. |
Now that your client requests are approved, you must review the server requests for each machine that you added to the cluster:
$ oc get csr
NAME AGE REQUESTOR CONDITION
csr-bfd72 5m26s system:node:ip-10-0-50-126.us-east-2.compute.internal Pending
csr-c57lv 5m26s system:node:ip-10-0-95-157.us-east-2.compute.internal Pending
...
If the remaining CSRs are not approved, and are in the Pending
status, approve the CSRs for your cluster machines:
To approve them individually, run the following command for each valid CSR:
$ oc adm certificate approve <csr_name> (1)
1 | <csr_name> is the name of a CSR from the list of current CSRs. |
To approve all pending CSRs, run the following command:
$ oc get csr -o go-template='{{range .items}}{{if not .status}}{{.metadata.name}}{{"\n"}}{{end}}{{end}}' | xargs oc adm certificate approve
After all client and server CSRs have been approved, the machines have the Ready
status. Verify this by running the following command:
$ oc get nodes
NAME STATUS ROLES AGE VERSION
master-0 Ready master 73m v1.26.0
master-1 Ready master 73m v1.26.0
master-2 Ready master 74m v1.26.0
worker-0 Ready worker 11m v1.26.0
worker-1 Ready worker 11m v1.26.0
It can take a few minutes after approval of the server CSRs for the machines to transition to the |
For more information on CSRs, see Certificate Signing Requests.
/var
partition in AWSOKD supports partitioning devices during installation by using machine configs that are processed during the bootstrap. However, if you use /var
partitioning, the device name must be determined at installation and cannot be changed. You cannot add different instance types as nodes if they have a different device naming schema. For example, if you configured the /var
partition with the default AWS device name for m4.large
instances, dev/xvdb
, you cannot directly add an AWS m5.large
instance, as m5.large
instances use a /dev/nvme1n1
device by default. The device might fail to partition due to the different naming schema.
The procedure in this section shows how to add a new Fedora CoreOS (FCOS) compute node with an instance that uses a different device name from what was configured at installation. You create a custom user data secret and configure a new compute machine set. These steps are specific to an AWS cluster. The principles apply to other cloud deployments also. However, the device naming schema is different for other deployments and should be determined on a per-case basis.
On a command line, change to the openshift-machine-api
namespace:
$ oc project openshift-machine-api
Create a new secret from the worker-user-data
secret:
Export the userData
section of the secret to a text file:
$ oc get secret worker-user-data --template='{{index .data.userData | base64decode}}' | jq > userData.txt
Edit the text file to add the storage
, filesystems
, and systemd
stanzas for the partitions you want to use for the new node. You can specify any Ignition configuration parameters as needed.
Do not change the values in the |
{
"ignition": {
"config": {
"merge": [
{
"source": "https:...."
}
]
},
"security": {
"tls": {
"certificateAuthorities": [
{
"source": "data:text/plain;charset=utf-8;base64,.....=="
}
]
}
},
"version": "3.2.0"
},
"storage": {
"disks": [
{
"device": "/dev/nvme1n1", (1)
"partitions": [
{
"label": "var",
"sizeMiB": 50000, (2)
"startMiB": 0 (3)
}
]
}
],
"filesystems": [
{
"device": "/dev/disk/by-partlabel/var", (4)
"format": "xfs", (5)
"path": "/var" (6)
}
]
},
"systemd": {
"units": [ (7)
{
"contents": "[Unit]\nBefore=local-fs.target\n[Mount]\nWhere=/var\nWhat=/dev/disk/by-partlabel/var\nOptions=defaults,pquota\n[Install]\nWantedBy=local-fs.target\n",
"enabled": true,
"name": "var.mount"
}
]
}
}
1 | Specifies an absolute path to the AWS block device. |
2 | Specifies the size of the data partition in Mebibytes. |
3 | Specifies the start of the partition in Mebibytes. When adding a data partition to the boot disk, a minimum value of 25000 MB (Mebibytes) is recommended. The root file system is automatically resized to fill all available space up to the specified offset. If no value is specified, or if the specified value is smaller than the recommended minimum, the resulting root file system will be too small, and future reinstalls of FCOS might overwrite the beginning of the data partition. |
4 | Specifies an absolute path to the /var partition. |
5 | Specifies the filesystem format. |
6 | Specifies the mount-point of the filesystem while Ignition is running relative to where the root filesystem will be mounted. This is not necessarily the same as where it should be mounted in the real root, but it is encouraged to make it the same. |
7 | Defines a systemd mount unit that mounts the /dev/disk/by-partlabel/var device to the /var partition. |
Extract the disableTemplating
section from the work-user-data
secret to a text file:
$ oc get secret worker-user-data --template='{{index .data.disableTemplating | base64decode}}' | jq > disableTemplating.txt
Create the new user data secret file from the two text files. This user data secret passes the additional node partition information in the userData.txt
file to the newly created node.
$ oc create secret generic worker-user-data-x5 --from-file=userData=userData.txt --from-file=disableTemplating=disableTemplating.txt
Create a new compute machine set for the new node:
Create a new compute machine set YAML file, similar to the following, which is configured for AWS. Add the required partitions and the newly-created user data secret:
Use an existing compute machine set as a template and change the parameters as needed for the new node. |
apiVersion: machine.openshift.io/v1beta1
kind: MachineSet
metadata:
labels:
machine.openshift.io/cluster-api-cluster: auto-52-92tf4
name: worker-us-east-2-nvme1n1 (1)
namespace: openshift-machine-api
spec:
replicas: 1
selector:
matchLabels:
machine.openshift.io/cluster-api-cluster: auto-52-92tf4
machine.openshift.io/cluster-api-machineset: auto-52-92tf4-worker-us-east-2b
template:
metadata:
labels:
machine.openshift.io/cluster-api-cluster: auto-52-92tf4
machine.openshift.io/cluster-api-machine-role: worker
machine.openshift.io/cluster-api-machine-type: worker
machine.openshift.io/cluster-api-machineset: auto-52-92tf4-worker-us-east-2b
spec:
metadata: {}
providerSpec:
value:
ami:
id: ami-0c2dbd95931a
apiVersion: awsproviderconfig.openshift.io/v1beta1
blockDevices:
- DeviceName: /dev/nvme1n1 (2)
ebs:
encrypted: true
iops: 0
volumeSize: 120
volumeType: gp2
- DeviceName: /dev/nvme1n2 (3)
ebs:
encrypted: true
iops: 0
volumeSize: 50
volumeType: gp2
credentialsSecret:
name: aws-cloud-credentials
deviceIndex: 0
iamInstanceProfile:
id: auto-52-92tf4-worker-profile
instanceType: m6i.large
kind: AWSMachineProviderConfig
metadata:
creationTimestamp: null
placement:
availabilityZone: us-east-2b
region: us-east-2
securityGroups:
- filters:
- name: tag:Name
values:
- auto-52-92tf4-worker-sg
subnet:
id: subnet-07a90e5db1
tags:
- name: kubernetes.io/cluster/auto-52-92tf4
value: owned
userDataSecret:
name: worker-user-data-x5 (4)
1 | Specifies a name for the new node. |
2 | Specifies an absolute path to the AWS block device, here an encrypted EBS volume. |
3 | Optional. Specifies an additional EBS volume. |
4 | Specifies the user data secret file. |
Create the compute machine set:
$ oc create -f <file-name>.yaml
The machines might take a few moments to become available.
Verify that the new partition and nodes are created:
Verify that the compute machine set is created:
$ oc get machineset
NAME DESIRED CURRENT READY AVAILABLE AGE
ci-ln-2675bt2-76ef8-bdgsc-worker-us-east-1a 1 1 1 1 124m
ci-ln-2675bt2-76ef8-bdgsc-worker-us-east-1b 2 2 2 2 124m
worker-us-east-2-nvme1n1 1 1 1 1 2m35s (1)
1 | This is the new compute machine set. |
Verify that the new node is created:
$ oc get nodes
NAME STATUS ROLES AGE VERSION
ip-10-0-128-78.ec2.internal Ready worker 117m v1.26.0
ip-10-0-146-113.ec2.internal Ready master 127m v1.26.0
ip-10-0-153-35.ec2.internal Ready worker 118m v1.26.0
ip-10-0-176-58.ec2.internal Ready master 126m v1.26.0
ip-10-0-217-135.ec2.internal Ready worker 2m57s v1.26.0 (1)
ip-10-0-225-248.ec2.internal Ready master 127m v1.26.0
ip-10-0-245-59.ec2.internal Ready worker 116m v1.26.0
1 | This is new new node. |
Verify that the custom /var
partition is created on the new node:
$ oc debug node/<node-name> -- chroot /host lsblk
For example:
$ oc debug node/ip-10-0-217-135.ec2.internal -- chroot /host lsblk
NAME MAJ:MIN RM SIZE RO TYPE MOUNTPOINT
nvme0n1 202:0 0 120G 0 disk
|-nvme0n1p1 202:1 0 1M 0 part
|-nvme0n1p2 202:2 0 127M 0 part
|-nvme0n1p3 202:3 0 384M 0 part /boot
`-nvme0n1p4 202:4 0 119.5G 0 part /sysroot
nvme1n1 202:16 0 50G 0 disk
`-nvme1n1p1 202:17 0 48.8G 0 part /var (1)
1 | The nvme1n1 device is mounted to the /var partition. |
For more information on how OKD uses disk partitioning, see Disk partitioning.
Understand and deploy machine health checks.
You can use the advanced machine management and scaling capabilities only in clusters where the Machine API is operational. Clusters with user-provisioned infrastructure require additional validation and configuration to use the Machine API. Clusters with the infrastructure platform type To view the platform type for your cluster, run the following command:
|
You can only apply a machine health check to machines that are managed by compute machine sets or control plane machine sets. |
To monitor machine health, create a resource to define the configuration for a controller. Set a condition to check, such as staying in the NotReady
status for five minutes or displaying a permanent condition in the node-problem-detector, and a label for the set of machines to monitor.
The controller that observes a MachineHealthCheck
resource checks for the defined condition. If a machine fails the health check, the machine is automatically deleted and one is created to take its place. When a machine is deleted, you see a machine deleted
event.
To limit disruptive impact of the machine deletion, the controller drains and deletes only one node at a time. If there are more unhealthy machines than the maxUnhealthy
threshold allows for in the targeted pool of machines, remediation stops and therefore enables manual intervention.
Consider the timeouts carefully, accounting for workloads and requirements.
|
To stop the check, remove the resource.
There are limitations to consider before deploying a machine health check:
Only machines owned by a machine set are remediated by a machine health check.
If the node for a machine is removed from the cluster, a machine health check considers the machine to be unhealthy and remediates it immediately.
If the corresponding node for a machine does not join the cluster after the nodeStartupTimeout
, the machine is remediated.
A machine is remediated immediately if the Machine
resource phase is Failed
.
The MachineHealthCheck
resource for all cloud-based installation types, and other than bare metal, resembles the following YAML file:
apiVersion: machine.openshift.io/v1beta1
kind: MachineHealthCheck
metadata:
name: example (1)
namespace: openshift-machine-api
spec:
selector:
matchLabels:
machine.openshift.io/cluster-api-machine-role: <role> (2)
machine.openshift.io/cluster-api-machine-type: <role> (2)
machine.openshift.io/cluster-api-machineset: <cluster_name>-<label>-<zone> (3)
unhealthyConditions:
- type: "Ready"
timeout: "300s" (4)
status: "False"
- type: "Ready"
timeout: "300s" (4)
status: "Unknown"
maxUnhealthy: "40%" (5)
nodeStartupTimeout: "10m" (6)
1 | Specify the name of the machine health check to deploy. |
2 | Specify a label for the machine pool that you want to check. |
3 | Specify the machine set to track in <cluster_name>-<label>-<zone> format. For example, prod-node-us-east-1a . |
4 | Specify the timeout duration for a node condition. If a condition is met for the duration of the timeout, the machine will be remediated. Long timeouts can result in long periods of downtime for a workload on an unhealthy machine. |
5 | Specify the amount of machines allowed to be concurrently remediated in the targeted pool. This can be set as a percentage or an integer. If the number of unhealthy machines exceeds the limit set by maxUnhealthy , remediation is not performed. |
6 | Specify the timeout duration that a machine health check must wait for a node to join the cluster before a machine is determined to be unhealthy. |
The |
Short-circuiting ensures that machine health checks remediate machines only when the cluster is healthy.
Short-circuiting is configured through the maxUnhealthy
field in the MachineHealthCheck
resource.
If the user defines a value for the maxUnhealthy
field, before remediating any machines, the MachineHealthCheck
compares the value of maxUnhealthy
with the number of machines within its target pool that it has determined to be unhealthy. Remediation is not performed if the number of unhealthy machines exceeds the maxUnhealthy
limit.
If |
The appropriate maxUnhealthy
value depends on the scale of the cluster you deploy and how many machines the MachineHealthCheck
covers. For example, you can use the maxUnhealthy
value to cover multiple compute machine sets across multiple availability zones so that if you lose an entire zone, your maxUnhealthy
setting prevents further remediation within the cluster. In global Azure regions that do not have multiple availability zones, you can use availability sets to ensure high availability.
If you configure a This configuration ensures that the machine health check takes no action when multiple control plane machines appear to be unhealthy. Multiple unhealthy control plane machines can indicate that the etcd cluster is degraded or that a scaling operation to replace a failed machine is in progress. If the etcd cluster is degraded, manual intervention might be required. If a scaling operation is in progress, the machine health check should allow it to finish. |
The maxUnhealthy
field can be set as either an integer or percentage.
There are different remediation implementations depending on the maxUnhealthy
value.
If maxUnhealthy
is set to 2
:
Remediation will be performed if 2 or fewer nodes are unhealthy
Remediation will not be performed if 3 or more nodes are unhealthy
These values are independent of how many machines are being checked by the machine health check.
If maxUnhealthy
is set to 40%
and there are 25 machines being checked:
Remediation will be performed if 10 or fewer nodes are unhealthy
Remediation will not be performed if 11 or more nodes are unhealthy
If maxUnhealthy
is set to 40%
and there are 6 machines being checked:
Remediation will be performed if 2 or fewer nodes are unhealthy
Remediation will not be performed if 3 or more nodes are unhealthy
The allowed number of machines is rounded down when the percentage of |
You can create a MachineHealthCheck
resource for machine sets in your cluster.
You can only apply a machine health check to machines that are managed by compute machine sets or control plane machine sets. |
Install the oc
command line interface.
Create a healthcheck.yml
file that contains the definition of your machine health check.
Apply the healthcheck.yml
file to your cluster:
$ oc apply -f healthcheck.yml
To add or remove an instance of a machine in a compute machine set, you can manually scale the compute machine set.
This guidance is relevant to fully automated, installer-provisioned infrastructure installations. Customized, user-provisioned infrastructure installations do not have compute machine sets.
Install an OKD cluster and the oc
command line.
Log in to oc
as a user with cluster-admin
permission.
View the compute machine sets that are in the cluster by running the following command:
$ oc get machinesets -n openshift-machine-api
The compute machine sets are listed in the form of <clusterid>-worker-<aws-region-az>
.
View the compute machines that are in the cluster by running the following command:
$ oc get machine -n openshift-machine-api
Set the annotation on the compute machine that you want to delete by running the following command:
$ oc annotate machine/<machine_name> -n openshift-machine-api machine.openshift.io/delete-machine="true"
Scale the compute machine set by running one of the following commands:
$ oc scale --replicas=2 machineset <machineset> -n openshift-machine-api
Or:
$ oc edit machineset <machineset> -n openshift-machine-api
You can alternatively apply the following YAML to scale the compute machine set:
|
You can scale the compute machine set up or down. It takes several minutes for the new machines to be available.
By default, the machine controller tries to drain the node that is backed by the machine until it succeeds. In some situations, such as with a misconfigured pod disruption budget, the drain operation might not be able to succeed. If the drain operation fails, the machine controller cannot proceed removing the machine. You can skip draining the node by annotating |
Verify the deletion of the intended machine by running the following command:
$ oc get machines
MachineSet
objects describe OKD nodes with respect to the cloud or machine provider.
The MachineConfigPool
object allows MachineConfigController
components to define and provide the status of machines in the context of upgrades.
The MachineConfigPool
object allows users to configure how upgrades are rolled out to the OKD nodes in the machine config pool.
The NodeSelector
object can be replaced with a reference to the MachineSet
object.
The OKD node configuration file contains important options. For
example, two parameters control the maximum number of pods that can be scheduled
to a node: podsPerCore
and maxPods
.
When both options are in use, the lower of the two values limits the number of pods on a node. Exceeding these values can result in:
Increased CPU utilization.
Slow pod scheduling.
Potential out-of-memory scenarios, depending on the amount of memory in the node.
Exhausting the pool of IP addresses.
Resource overcommitting, leading to poor user application performance.
In Kubernetes, a pod that is holding a single container actually uses two containers. The second container is used to set up networking prior to the actual container starting. Therefore, a system running 10 pods will actually have 20 containers running. |
Disk IOPS throttling from the cloud provider might have an impact on CRI-O and kubelet. They might get overloaded when there are large number of I/O intensive pods running on the nodes. It is recommended that you monitor the disk I/O on the nodes and use volumes with sufficient throughput for the workload. |
The podsPerCore
parameter sets the number of pods the node can run based on the number of
processor cores on the node. For example, if podsPerCore
is set to 10
on a
node with 4 processor cores, the maximum number of pods allowed on the node will
be 40
.
kubeletConfig:
podsPerCore: 10
Setting podsPerCore
to 0
disables this limit. The default is 0
.
The value of the podsPerCore
parameter cannot exceed the value of the maxPods
parameter.
The maxPods
parameter sets the number of pods the node can run to a fixed value, regardless
of the properties of the node.
kubeletConfig:
maxPods: 250
The kubelet configuration is currently serialized as an Ignition configuration, so it can be directly edited. However, there is also a new kubelet-config-controller
added to the Machine Config Controller (MCC). This lets you use a KubeletConfig
custom resource (CR) to edit the kubelet parameters.
As the fields in the |
Consider the following guidance:
Edit an existing KubeletConfig
CR to modify existing settings or add new settings, instead of creating a CR for each change. It is recommended that you create a CR only to modify a different machine config pool, or for changes that are intended to be temporary, so that you can revert the changes.
Create one KubeletConfig
CR for each machine config pool with all the config changes you want for that pool.
As needed, create multiple KubeletConfig
CRs with a limit of 10 per cluster. For the first KubeletConfig
CR, the Machine Config Operator (MCO) creates a machine config appended with kubelet
. With each subsequent CR, the controller creates another kubelet
machine config with a numeric suffix. For example, if you have a kubelet
machine config with a -2
suffix, the next kubelet
machine config is appended with -3
.
If you are applying a kubelet or container runtime config to a custom machine config pool, the custom role in the For example, because the following custom machine config pool is named
|
If you want to delete the machine configs, delete them in reverse order to avoid exceeding the limit. For example, you delete the kubelet-3
machine config before deleting the kubelet-2
machine config.
If you have a machine config with a |
KubeletConfig
CR$ oc get kubeletconfig
NAME AGE
set-max-pods 15m
KubeletConfig
machine config$ oc get mc | grep kubelet
...
99-worker-generated-kubelet-1 b5c5119de007945b6fe6fb215db3b8e2ceb12511 3.2.0 26m
...
The following procedure is an example to show how to configure the maximum number of pods per node on the worker nodes.
Obtain the label associated with the static MachineConfigPool
CR for the type of node you want to configure.
Perform one of the following steps:
View the machine config pool:
$ oc describe machineconfigpool <name>
For example:
$ oc describe machineconfigpool worker
apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfigPool
metadata:
creationTimestamp: 2019-02-08T14:52:39Z
generation: 1
labels:
custom-kubelet: set-max-pods (1)
1 | If a label has been added it appears under labels . |
If the label is not present, add a key/value pair:
$ oc label machineconfigpool worker custom-kubelet=set-max-pods
View the available machine configuration objects that you can select:
$ oc get machineconfig
By default, the two kubelet-related configs are 01-master-kubelet
and 01-worker-kubelet
.
Check the current value for the maximum pods per node:
$ oc describe node <node_name>
For example:
$ oc describe node ci-ln-5grqprb-f76d1-ncnqq-worker-a-mdv94
Look for value: pods: <value>
in the Allocatable
stanza:
Allocatable:
attachable-volumes-aws-ebs: 25
cpu: 3500m
hugepages-1Gi: 0
hugepages-2Mi: 0
memory: 15341844Ki
pods: 250
Set the maximum pods per node on the worker nodes by creating a custom resource file that contains the kubelet configuration:
Kubelet configurations that target a specific machine config pool also affect any dependent pools. For example, creating a kubelet configuration for the pool containing worker nodes will also apply to any subset pools, including the pool containing infrastructure nodes. To avoid this, you must create a new machine config pool with a selection expression that only includes worker nodes, and have your kubelet configuration target this new pool. |
apiVersion: machineconfiguration.openshift.io/v1
kind: KubeletConfig
metadata:
name: set-max-pods
spec:
machineConfigPoolSelector:
matchLabels:
custom-kubelet: set-max-pods (1)
kubeletConfig:
maxPods: 500 (2)
1 | Enter the label from the machine config pool. |
2 | Add the kubelet configuration. In this example, use maxPods to set the maximum pods per node. |
The rate at which the kubelet talks to the API server depends on queries per second (QPS) and burst values. The default values,
|
Update the machine config pool for workers with the label:
$ oc label machineconfigpool worker custom-kubelet=set-max-pods
Create the KubeletConfig
object:
$ oc create -f change-maxPods-cr.yaml
Verify that the KubeletConfig
object is created:
$ oc get kubeletconfig
NAME AGE
set-max-pods 15m
Depending on the number of worker nodes in the cluster, wait for the worker nodes to be rebooted one by one. For a cluster with 3 worker nodes, this could take about 10 to 15 minutes.
Verify that the changes are applied to the node:
Check on a worker node that the maxPods
value changed:
$ oc describe node <node_name>
Locate the Allocatable
stanza:
...
Allocatable:
attachable-volumes-gce-pd: 127
cpu: 3500m
ephemeral-storage: 123201474766
hugepages-1Gi: 0
hugepages-2Mi: 0
memory: 14225400Ki
pods: 500 (1)
...
1 | In this example, the pods parameter should report the value you set in the KubeletConfig object. |
Verify the change in the KubeletConfig
object:
$ oc get kubeletconfigs set-max-pods -o yaml
This should show a status of True
and type:Success
, as shown in the following example:
spec:
kubeletConfig:
maxPods: 500
machineConfigPoolSelector:
matchLabels:
custom-kubelet: set-max-pods
status:
conditions:
- lastTransitionTime: "2021-06-30T17:04:07Z"
message: Success
status: "True"
type: Success
By default, only one machine is allowed to be unavailable when applying the kubelet-related configuration to the available worker nodes. For a large cluster, it can take a long time for the configuration change to be reflected. At any time, you can adjust the number of machines that are updating to speed up the process.
Edit the worker
machine config pool:
$ oc edit machineconfigpool worker
Add the maxUnavailable
field and set the value:
spec:
maxUnavailable: <node_count>
When setting the value, consider the number of worker nodes that can be unavailable without affecting the applications running on the cluster. |
The control plane node resource requirements depend on the number and type of nodes and objects in the cluster. The following control plane node size recommendations are based on the results of a control plane density focused testing, or Cluster-density. This test creates the following objects across a given number of namespaces:
1 image stream
1 build
5 deployments, with 2 pod replicas in a sleep
state, mounting 4 secrets, 4 config maps, and 1 downward API volume each
5 services, each one pointing to the TCP/8080 and TCP/8443 ports of one of the previous deployments
1 route pointing to the first of the previous services
10 secrets containing 2048 random string characters
10 config maps containing 2048 random string characters
Number of worker nodes | Cluster-density (namespaces) | CPU cores | Memory (GB) |
---|---|---|---|
24 |
500 |
4 |
16 |
120 |
1000 |
8 |
32 |
252 |
4000 |
16, but 24 if using the OVN-Kubernetes network plug-in |
64, but 128 if using the OVN-Kubernetes network plug-in |
501, but untested with the OVN-Kubernetes network plug-in |
4000 |
16 |
96 |
The data from the table above is based on an OKD running on top of AWS, using r5.4xlarge instances as control-plane nodes and m5.2xlarge instances as worker nodes.
On a large and dense cluster with three control plane nodes, the CPU and memory usage will spike up when one of the nodes is stopped, rebooted, or fails. The failures can be due to unexpected issues with power, network, underlying infrastructure, or intentional cases where the cluster is restarted after shutting it down to save costs. The remaining two control plane nodes must handle the load in order to be highly available, which leads to increase in the resource usage. This is also expected during upgrades because the control plane nodes are cordoned, drained, and rebooted serially to apply the operating system updates, as well as the control plane Operators update. To avoid cascading failures, keep the overall CPU and memory resource usage on the control plane nodes to at most 60% of all available capacity to handle the resource usage spikes. Increase the CPU and memory on the control plane nodes accordingly to avoid potential downtime due to lack of resources.
The node sizing varies depending on the number of nodes and object counts in the cluster. It also depends on whether the objects are actively being created on the cluster. During object creation, the control plane is more active in terms of resource usage compared to when the objects are in the |
Operator Lifecycle Manager (OLM ) runs on the control plane nodes and its memory footprint depends on the number of namespaces and user installed operators that OLM needs to manage on the cluster. Control plane nodes need to be sized accordingly to avoid OOM kills. Following data points are based on the results from cluster maximums testing.
Number of namespaces | OLM memory at idle state (GB) | OLM memory with 5 user operators installed (GB) |
---|---|---|
500 |
0.823 |
1.7 |
1000 |
1.2 |
2.5 |
1500 |
1.7 |
3.2 |
2000 |
2 |
4.4 |
3000 |
2.7 |
5.6 |
4000 |
3.8 |
7.6 |
5000 |
4.2 |
9.02 |
6000 |
5.8 |
11.3 |
7000 |
6.6 |
12.9 |
8000 |
6.9 |
14.8 |
9000 |
8 |
17.7 |
10,000 |
9.9 |
21.6 |
You can modify the control plane node size in a running OKD 4.13 cluster for the following configurations only:
For all other configurations, you must estimate your total node count and use the suggested control plane node size during installation. |
The recommendations are based on the data points captured on OKD clusters with OpenShift SDN as the network plugin. |
In OKD 4.13, half of a CPU core (500 millicore) is now reserved by the system by default compared to OKD 3.11 and previous versions. The sizes are determined taking that into consideration. |
Optional: Label a node:
# oc label node perf-node.example.com cpumanager=true
Edit the MachineConfigPool
of the nodes where CPU Manager should be enabled. In this example, all workers have CPU Manager enabled:
# oc edit machineconfigpool worker
Add a label to the worker machine config pool:
metadata:
creationTimestamp: 2020-xx-xxx
generation: 3
labels:
custom-kubelet: cpumanager-enabled
Create a KubeletConfig
, cpumanager-kubeletconfig.yaml
, custom resource (CR). Refer to the label created in the previous step to have the correct nodes updated with the new kubelet config. See the machineConfigPoolSelector
section:
apiVersion: machineconfiguration.openshift.io/v1
kind: KubeletConfig
metadata:
name: cpumanager-enabled
spec:
machineConfigPoolSelector:
matchLabels:
custom-kubelet: cpumanager-enabled
kubeletConfig:
cpuManagerPolicy: static (1)
cpuManagerReconcilePeriod: 5s (2)
1 | Specify a policy:
|
2 | Optional. Specify the CPU Manager reconcile frequency. The default is 5s . |
Create the dynamic kubelet config:
# oc create -f cpumanager-kubeletconfig.yaml
This adds the CPU Manager feature to the kubelet config and, if needed, the Machine Config Operator (MCO) reboots the node. To enable CPU Manager, a reboot is not needed.
Check for the merged kubelet config:
# oc get machineconfig 99-worker-XXXXXX-XXXXX-XXXX-XXXXX-kubelet -o json | grep ownerReference -A7
"ownerReferences": [
{
"apiVersion": "machineconfiguration.openshift.io/v1",
"kind": "KubeletConfig",
"name": "cpumanager-enabled",
"uid": "7ed5616d-6b72-11e9-aae1-021e1ce18878"
}
]
Check the worker for the updated kubelet.conf
:
# oc debug node/perf-node.example.com
sh-4.2# cat /host/etc/kubernetes/kubelet.conf | grep cpuManager
cpuManagerPolicy: static (1)
cpuManagerReconcilePeriod: 5s (2)
1 | cpuManagerPolicy is defined when you create the KubeletConfig CR. |
2 | cpuManagerReconcilePeriod is defined when you create the KubeletConfig CR. |
Create a pod that requests a core or multiple cores. Both limits and requests must have their CPU value set to a whole integer. That is the number of cores that will be dedicated to this pod:
# cat cpumanager-pod.yaml
apiVersion: v1
kind: Pod
metadata:
generateName: cpumanager-
spec:
containers:
- name: cpumanager
image: gcr.io/google_containers/pause-amd64:3.0
resources:
requests:
cpu: 1
memory: "1G"
limits:
cpu: 1
memory: "1G"
nodeSelector:
cpumanager: "true"
Create the pod:
# oc create -f cpumanager-pod.yaml
Verify that the pod is scheduled to the node that you labeled:
# oc describe pod cpumanager
Name: cpumanager-6cqz7
Namespace: default
Priority: 0
PriorityClassName: <none>
Node: perf-node.example.com/xxx.xx.xx.xxx
...
Limits:
cpu: 1
memory: 1G
Requests:
cpu: 1
memory: 1G
...
QoS Class: Guaranteed
Node-Selectors: cpumanager=true
Verify that the cgroups
are set up correctly. Get the process ID (PID) of the pause
process:
# ├─init.scope
│ └─1 /usr/lib/systemd/systemd --switched-root --system --deserialize 17
└─kubepods.slice
├─kubepods-pod69c01f8e_6b74_11e9_ac0f_0a2b62178a22.slice
│ ├─crio-b5437308f1a574c542bdf08563b865c0345c8f8c0b0a655612c.scope
│ └─32706 /pause
Pods of quality of service (QoS) tier Guaranteed
are placed within the kubepods.slice
. Pods of other QoS tiers end up in child cgroups
of kubepods
:
# cd /sys/fs/cgroup/cpuset/kubepods.slice/kubepods-pod69c01f8e_6b74_11e9_ac0f_0a2b62178a22.slice/crio-b5437308f1ad1a7db0574c542bdf08563b865c0345c86e9585f8c0b0a655612c.scope
# for i in `ls cpuset.cpus tasks` ; do echo -n "$i "; cat $i ; done
cpuset.cpus 1
tasks 32706
Check the allowed CPU list for the task:
# grep ^Cpus_allowed_list /proc/32706/status
Cpus_allowed_list: 1
Verify that another pod (in this case, the pod in the burstable
QoS tier) on the system cannot run on the core allocated for the Guaranteed
pod:
# cat /sys/fs/cgroup/cpuset/kubepods.slice/kubepods-besteffort.slice/kubepods-besteffort-podc494a073_6b77_11e9_98c0_06bba5c387ea.slice/crio-c56982f57b75a2420947f0afc6cafe7534c5734efc34157525fa9abbf99e3849.scope/cpuset.cpus
0
# oc describe node perf-node.example.com
...
Capacity:
attachable-volumes-aws-ebs: 39
cpu: 2
ephemeral-storage: 124768236Ki
hugepages-1Gi: 0
hugepages-2Mi: 0
memory: 8162900Ki
pods: 250
Allocatable:
attachable-volumes-aws-ebs: 39
cpu: 1500m
ephemeral-storage: 124768236Ki
hugepages-1Gi: 0
hugepages-2Mi: 0
memory: 7548500Ki
pods: 250
------- ---- ------------ ---------- --------------- ------------- ---
default cpumanager-6cqz7 1 (66%) 1 (66%) 1G (12%) 1G (12%) 29m
Allocated resources:
(Total limits may be over 100 percent, i.e., overcommitted.)
Resource Requests Limits
-------- -------- ------
cpu 1440m (96%) 1 (66%)
This VM has two CPU cores. The system-reserved
setting reserves 500 millicores, meaning that half of one core is subtracted from the total capacity of the node to arrive at the Node Allocatable
amount. You can see that Allocatable CPU
is 1500 millicores. This means you can run one of the CPU Manager pods since each will take one whole core. A whole core is equivalent to 1000 millicores. If you try to schedule a second pod, the system will accept the pod, but it will never be scheduled:
NAME READY STATUS RESTARTS AGE
cpumanager-6cqz7 1/1 Running 0 33m
cpumanager-7qc2t 0/1 Pending 0 11s
Understand and configure huge pages.
Memory is managed in blocks known as pages. On most systems, a page is 4Ki. 1Mi of memory is equal to 256 pages; 1Gi of memory is 256,000 pages, and so on. CPUs have a built-in memory management unit that manages a list of these pages in hardware. The Translation Lookaside Buffer (TLB) is a small hardware cache of virtual-to-physical page mappings. If the virtual address passed in a hardware instruction can be found in the TLB, the mapping can be determined quickly. If not, a TLB miss occurs, and the system falls back to slower, software-based address translation, resulting in performance issues. Since the size of the TLB is fixed, the only way to reduce the chance of a TLB miss is to increase the page size.
A huge page is a memory page that is larger than 4Ki. On x86_64 architectures, there are two common huge page sizes: 2Mi and 1Gi. Sizes vary on other architectures. To use huge pages, code must be written so that applications are aware of them. Transparent Huge Pages (THP) attempt to automate the management of huge pages without application knowledge, but they have limitations. In particular, they are limited to 2Mi page sizes. THP can lead to performance degradation on nodes with high memory utilization or fragmentation due to defragmenting efforts of THP, which can lock memory pages. For this reason, some applications may be designed to (or recommend) usage of pre-allocated huge pages instead of THP.
Nodes must pre-allocate huge pages in order for the node to report its huge page capacity. A node can only pre-allocate huge pages for a single size.
Huge pages can be consumed through container-level resource requirements using the
resource name hugepages-<size>
, where size is the most compact binary
notation using integer values supported on a particular node. For example, if a
node supports 2048KiB page sizes, it exposes a schedulable resource
hugepages-2Mi
. Unlike CPU or memory, huge pages do not support over-commitment.
apiVersion: v1
kind: Pod
metadata:
generateName: hugepages-volume-
spec:
containers:
- securityContext:
privileged: true
image: rhel7:latest
command:
- sleep
- inf
name: example
volumeMounts:
- mountPath: /dev/hugepages
name: hugepage
resources:
limits:
hugepages-2Mi: 100Mi (1)
memory: "1Gi"
cpu: "1"
volumes:
- name: hugepage
emptyDir:
medium: HugePages
1 | Specify the amount of memory for hugepages as the exact amount to be
allocated. Do not specify this value as the amount of memory for hugepages
multiplied by the size of the page. For example, given a huge page size of 2MB,
if you want to use 100MB of huge-page-backed RAM for your application, then you
would allocate 50 huge pages. OKD handles the math for you. As in
the above example, you can specify 100MB directly. |
Allocating huge pages of a specific size
Some platforms support multiple huge page sizes. To allocate huge pages of a
specific size, precede the huge pages boot command parameters with a huge page
size selection parameter hugepagesz=<size>
. The <size>
value must be
specified in bytes with an optional scale suffix [kKmMgG
]. The default huge
page size can be defined with the default_hugepagesz=<size>
boot parameter.
Huge page requirements
Huge page requests must equal the limits. This is the default if limits are specified, but requests are not.
Huge pages are isolated at a pod scope. Container isolation is planned in a future iteration.
EmptyDir
volumes backed by huge pages must not consume more huge page memory
than the pod request.
Applications that consume huge pages via shmget()
with SHM_HUGETLB
must run
with a supplemental group that matches proc/sys/vm/hugetlb_shm_group.
Nodes must pre-allocate huge pages used in an OKD cluster. There are two ways of reserving huge pages: at boot time and at run time. Reserving at boot time increases the possibility of success because the memory has not yet been significantly fragmented. The Node Tuning Operator currently supports boot time allocation of huge pages on specific nodes.
To minimize node reboots, the order of the steps below needs to be followed:
Label all nodes that need the same huge pages setting by a label.
$ oc label node <node_using_hugepages> node-role.kubernetes.io/worker-hp=
Create a file with the following content and name it hugepages-tuned-boottime.yaml
:
apiVersion: tuned.openshift.io/v1
kind: Tuned
metadata:
name: hugepages (1)
namespace: openshift-cluster-node-tuning-operator
spec:
profile: (2)
- data: |
[main]
summary=Boot time configuration for hugepages
include=openshift-node
[bootloader]
cmdline_openshift_node_hugepages=hugepagesz=2M hugepages=50 (3)
name: openshift-node-hugepages
recommend:
- machineConfigLabels: (4)
machineconfiguration.openshift.io/role: "worker-hp"
priority: 30
profile: openshift-node-hugepages
1 | Set the name of the Tuned resource to hugepages . |
2 | Set the profile section to allocate huge pages. |
3 | Note the order of parameters is important as some platforms support huge pages of various sizes. |
4 | Enable machine config pool based matching. |
Create the Tuned hugepages
object
$ oc create -f hugepages-tuned-boottime.yaml
Create a file with the following content and name it hugepages-mcp.yaml
:
apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfigPool
metadata:
name: worker-hp
labels:
worker-hp: ""
spec:
machineConfigSelector:
matchExpressions:
- {key: machineconfiguration.openshift.io/role, operator: In, values: [worker,worker-hp]}
nodeSelector:
matchLabels:
node-role.kubernetes.io/worker-hp: ""
Create the machine config pool:
$ oc create -f hugepages-mcp.yaml
Given enough non-fragmented memory, all the nodes in the worker-hp
machine config pool should now have 50 2Mi huge pages allocated.
$ oc get node <node_using_hugepages> -o jsonpath="{.status.allocatable.hugepages-2Mi}"
100Mi
The device plugin provides a consistent and portable solution to consume hardware devices across clusters. The device plugin provides support for these devices through an extension mechanism, which makes these devices available to Containers, provides health checks of these devices, and securely shares them.
OKD supports the device plugin API, but the device plugin Containers are supported by individual vendors. |
A device plugin is a gRPC service running on the nodes (external to
the kubelet
) that is responsible for managing specific
hardware resources. Any device plugin must support following remote procedure
calls (RPCs):
service DevicePlugin {
// GetDevicePluginOptions returns options to be communicated with Device
// Manager
rpc GetDevicePluginOptions(Empty) returns (DevicePluginOptions) {}
// ListAndWatch returns a stream of List of Devices
// Whenever a Device state change or a Device disappears, ListAndWatch
// returns the new list
rpc ListAndWatch(Empty) returns (stream ListAndWatchResponse) {}
// Allocate is called during container creation so that the Device
// Plug-in can run device specific operations and instruct Kubelet
// of the steps to make the Device available in the container
rpc Allocate(AllocateRequest) returns (AllocateResponse) {}
// PreStartcontainer is called, if indicated by Device Plug-in during
// registration phase, before each container start. Device plug-in
// can run device specific operations such as resetting the device
// before making devices available to the container
rpc PreStartcontainer(PreStartcontainerRequest) returns (PreStartcontainerResponse) {}
}
For easy device plugin reference implementation, there is a stub device plugin in the Device Manager code: vendor/k8s.io/kubernetes/pkg/kubelet/cm/deviceplugin/device_plugin_stub.go. |
Daemon sets are the recommended approach for device plugin deployments.
Upon start, the device plugin will try to create a UNIX domain socket at /var/lib/kubelet/device-plugin/ on the node to serve RPCs from Device Manager.
Since device plugins must manage hardware resources, access to the host file system, as well as socket creation, they must be run in a privileged security context.
More specific details regarding deployment steps can be found with each device plugin implementation.
Device Manager provides a mechanism for advertising specialized node hardware resources with the help of plugins known as device plugins.
You can advertise specialized hardware without requiring any upstream code changes.
OKD supports the device plugin API, but the device plugin Containers are supported by individual vendors. |
Device Manager advertises devices as Extended Resources. User pods can consume devices, advertised by Device Manager, using the same Limit/Request mechanism, which is used for requesting any other Extended Resource.
Upon start, the device plugin registers itself with Device Manager invoking Register
on the
/var/lib/kubelet/device-plugins/kubelet.sock and starts a gRPC service at
/var/lib/kubelet/device-plugins/<plugin>.sock for serving Device Manager
requests.
Device Manager, while processing a new registration request, invokes
ListAndWatch
remote procedure call (RPC) at the device plugin service. In
response, Device Manager gets a list of Device objects from the plugin over a
gRPC stream. Device Manager will keep watching on the stream for new updates
from the plugin. On the plugin side, the plugin will also keep the stream
open and whenever there is a change in the state of any of the devices, a new
device list is sent to the Device Manager over the same streaming connection.
While handling a new pod admission request, Kubelet passes requested Extended
Resources
to the Device Manager for device allocation. Device Manager checks in
its database to verify if a corresponding plugin exists or not. If the plugin exists
and there are free allocatable devices as well as per local cache, Allocate
RPC is invoked at that particular device plugin.
Additionally, device plugins can also perform several other device-specific operations, such as driver installation, device initialization, and device resets. These functionalities vary from implementation to implementation.
Enable Device Manager to implement a device plugin to advertise specialized hardware without any upstream code changes.
Device Manager provides a mechanism for advertising specialized node hardware resources with the help of plugins known as device plugins.
Obtain the label associated with the static MachineConfigPool
CRD for the type of node you want to configure by entering the following command.
Perform one of the following steps:
View the machine config:
# oc describe machineconfig <name>
For example:
# oc describe machineconfig 00-worker
Name: 00-worker
Namespace:
Labels: machineconfiguration.openshift.io/role=worker (1)
1 | Label required for the Device Manager. |
Create a custom resource (CR) for your configuration change.
apiVersion: machineconfiguration.openshift.io/v1
kind: KubeletConfig
metadata:
name: devicemgr (1)
spec:
machineConfigPoolSelector:
matchLabels:
machineconfiguration.openshift.io: devicemgr (2)
kubeletConfig:
feature-gates:
- DevicePlugins=true (3)
1 | Assign a name to CR. |
2 | Enter the label from the Machine Config Pool. |
3 | Set DevicePlugins to 'true`. |
Create the Device Manager:
$ oc create -f devicemgr.yaml
kubeletconfig.machineconfiguration.openshift.io/devicemgr created
Ensure that Device Manager was actually enabled by confirming that /var/lib/kubelet/device-plugins/kubelet.sock is created on the node. This is the UNIX domain socket on which the Device Manager gRPC server listens for new plugin registrations. This sock file is created when the Kubelet is started only if Device Manager is enabled.
Understand and work with taints and tolerations.
A taint allows a node to refuse a pod to be scheduled unless that pod has a matching toleration.
You apply taints to a node through the Node
specification (NodeSpec
) and apply tolerations to a pod through the Pod
specification (PodSpec
). When you apply a taint a node, the scheduler cannot place a pod on that node unless the pod can tolerate the taint.
apiVersion: v1
kind: Node
metadata:
name: my-node
#...
spec:
taints:
- effect: NoExecute
key: key1
value: value1
#...
Pod
specapiVersion: v1
kind: Pod
metadata:
name: my-pod
#...
spec:
tolerations:
- key: "key1"
operator: "Equal"
value: "value1"
effect: "NoExecute"
tolerationSeconds: 3600
#...
Taints and tolerations consist of a key, value, and effect.
Parameter | Description | ||||||
---|---|---|---|---|---|---|---|
|
The |
||||||
|
The |
||||||
|
The effect is one of the following:
|
||||||
|
|
If you add a NoSchedule
taint to a control plane node, the node must have the node-role.kubernetes.io/master=:NoSchedule
taint, which is added by default.
For example:
apiVersion: v1
kind: Node
metadata:
annotations:
machine.openshift.io/machine: openshift-machine-api/ci-ln-62s7gtb-f76d1-v8jxv-master-0
machineconfiguration.openshift.io/currentConfig: rendered-master-cdc1ab7da414629332cc4c3926e6e59c
name: my-node
#...
spec:
taints:
- effect: NoSchedule
key: node-role.kubernetes.io/master
#...
A toleration matches a taint:
If the operator
parameter is set to Equal
:
the key
parameters are the same;
the value
parameters are the same;
the effect
parameters are the same.
If the operator
parameter is set to Exists
:
the key
parameters are the same;
the effect
parameters are the same.
The following taints are built into OKD:
node.kubernetes.io/not-ready
: The node is not ready. This corresponds to the node condition Ready=False
.
node.kubernetes.io/unreachable
: The node is unreachable from the node controller. This corresponds to the node condition Ready=Unknown
.
node.kubernetes.io/memory-pressure
: The node has memory pressure issues. This corresponds to the node condition MemoryPressure=True
.
node.kubernetes.io/disk-pressure
: The node has disk pressure issues. This corresponds to the node condition DiskPressure=True
.
node.kubernetes.io/network-unavailable
: The node network is unavailable.
node.kubernetes.io/unschedulable
: The node is unschedulable.
node.cloudprovider.kubernetes.io/uninitialized
: When the node controller is started with an external cloud provider, this taint is set on a node to mark it as unusable. After a controller from the cloud-controller-manager initializes this node, the kubelet removes this taint.
node.kubernetes.io/pid-pressure
: The node has pid pressure. This corresponds to the node condition PIDPressure=True
.
OKD does not set a default pid.available |
You add tolerations to pods and taints to nodes to allow the node to control which pods should or should not be scheduled on them. For existing pods and nodes, you should add the toleration to the pod first, then add the taint to the node to avoid pods being removed from the node before you can add the toleration.
Add a toleration to a pod by editing the Pod
spec to include a tolerations
stanza:
apiVersion: v1
kind: Pod
metadata:
name: my-pod
#...
spec:
tolerations:
- key: "key1" (1)
value: "value1"
operator: "Equal"
effect: "NoExecute"
tolerationSeconds: 3600 (2)
#...
1 | The toleration parameters, as described in the Taint and toleration components table. |
2 | The tolerationSeconds parameter specifies how long a pod can remain bound to a node before being evicted. |
For example:
apiVersion: v1
kind: Pod
metadata:
name: my-pod
#...
spec:
tolerations:
- key: "key1"
operator: "Exists" (1)
effect: "NoExecute"
tolerationSeconds: 3600
#...
1 | The Exists operator does not take a value . |
This example places a taint on node1
that has key key1
, value value1
, and taint effect NoExecute
.
Add a taint to a node by using the following command with the parameters described in the Taint and toleration components table:
$ oc adm taint nodes <node_name> <key>=<value>:<effect>
For example:
$ oc adm taint nodes node1 key1=value1:NoExecute
This command places a taint on node1
that has key key1
, value value1
, and effect NoExecute
.
If you add a For example:
|
The tolerations on the pod match the taint on the node. A pod with either toleration can be scheduled onto node1
.
You can add taints to nodes using a compute machine set. All nodes associated with the MachineSet
object are updated with the taint. Tolerations respond to taints added by a compute machine set in the same manner as taints added directly to the nodes.
Add a toleration to a pod by editing the Pod
spec to include a tolerations
stanza:
Equal
operatorapiVersion: v1
kind: Pod
metadata:
name: my-pod
#...
spec:
tolerations:
- key: "key1" (1)
value: "value1"
operator: "Equal"
effect: "NoExecute"
tolerationSeconds: 3600 (2)
#...
1 | The toleration parameters, as described in the Taint and toleration components table. |
2 | The tolerationSeconds parameter specifies how long a pod is bound to a node before being evicted. |
For example:
Exists
operatorapiVersion: v1
kind: Pod
metadata:
name: my-pod
#...
spec:
tolerations:
- key: "key1"
operator: "Exists"
effect: "NoExecute"
tolerationSeconds: 3600
#...
Add the taint to the MachineSet
object:
Edit the MachineSet
YAML for the nodes you want to taint or you can create a new MachineSet
object:
$ oc edit machineset <machineset>
Add the taint to the spec.template.spec
section:
apiVersion: machine.openshift.io/v1beta1
kind: MachineSet
metadata:
name: my-machineset
#...
spec:
#...
template:
#...
spec:
taints:
- effect: NoExecute
key: key1
value: value1
#...
This example places a taint that has the key key1
, value value1
, and taint effect NoExecute
on the nodes.
Scale down the compute machine set to 0:
$ oc scale --replicas=0 machineset <machineset> -n openshift-machine-api
You can alternatively apply the following YAML to scale the compute machine set:
|
Wait for the machines to be removed.
Scale up the compute machine set as needed:
$ oc scale --replicas=2 machineset <machineset> -n openshift-machine-api
Or:
$ oc edit machineset <machineset> -n openshift-machine-api
Wait for the machines to start. The taint is added to the nodes associated with the MachineSet
object.
If you want to dedicate a set of nodes for exclusive use by a particular set of users, add a toleration to their pods. Then, add a corresponding taint to those nodes. The pods with the tolerations are allowed to use the tainted nodes or any other nodes in the cluster.
If you want ensure the pods are scheduled to only those tainted nodes, also add a label to the same set of nodes and add a node affinity to the pods so that the pods can only be scheduled onto nodes with that label.
To configure a node so that users can use only that node:
Add a corresponding taint to those nodes:
For example:
$ oc adm taint nodes node1 dedicated=groupName:NoSchedule
You can alternatively apply the following YAML to add the taint:
|
Add a toleration to the pods by writing a custom admission controller.
In a cluster where a small subset of nodes have specialized hardware, you can use taints and tolerations to keep pods that do not need the specialized hardware off of those nodes, leaving the nodes for pods that do need the specialized hardware. You can also require pods that need specialized hardware to use specific nodes.
You can achieve this by adding a toleration to pods that need the special hardware and tainting the nodes that have the specialized hardware.
To ensure nodes with specialized hardware are reserved for specific pods:
Add a toleration to pods that need the special hardware.
For example:
apiVersion: v1
kind: Pod
metadata:
name: my-pod
#...
spec:
tolerations:
- key: "disktype"
value: "ssd"
operator: "Equal"
effect: "NoSchedule"
tolerationSeconds: 3600
#...
Taint the nodes that have the specialized hardware using one of the following commands:
$ oc adm taint nodes <node-name> disktype=ssd:NoSchedule
Or:
$ oc adm taint nodes <node-name> disktype=ssd:PreferNoSchedule
You can alternatively apply the following YAML to add the taint:
|
You can remove taints from nodes and tolerations from pods as needed. You should add the toleration to the pod first, then add the taint to the node to avoid pods being removed from the node before you can add the toleration.
To remove taints and tolerations:
To remove a taint from a node:
$ oc adm taint nodes <node-name> <key>-
For example:
$ oc adm taint nodes ip-10-0-132-248.ec2.internal key1-
node/ip-10-0-132-248.ec2.internal untainted
To remove a toleration from a pod, edit the Pod
spec to remove the toleration:
apiVersion: v1
kind: Pod
metadata:
name: my-pod
#...
spec:
tolerations:
- key: "key2"
operator: "Exists"
effect: "NoExecute"
tolerationSeconds: 3600
#...
Understand and work with Topology Manager.
Topology Manager aligns Pod
resources of all Quality of Service (QoS) classes by collecting topology hints from Hint Providers, such as CPU Manager and Device Manager, and using the collected hints to align the Pod
resources.
Topology Manager supports four allocation policies, which you assign in the KubeletConfig
custom resource (CR) named cpumanager-enabled
:
none
policyThis is the default policy and does not perform any topology alignment.
best-effort
policyFor each container in a pod with the best-effort
topology management policy, kubelet calls each Hint Provider to discover their resource
availability. Using this information, the Topology Manager stores the preferred NUMA Node affinity for that container. If the affinity is not preferred, Topology Manager stores this and admits the pod to the node.
restricted
policyFor each container in a pod with the restricted
topology management policy, kubelet calls each Hint Provider to discover their resource
availability. Using this information, the Topology Manager stores the preferred NUMA Node affinity for that container. If the affinity is not
preferred, Topology Manager rejects this pod from the node, resulting in a pod in a Terminated
state with a pod admission failure.
single-numa-node
policyFor each container in a pod with the single-numa-node
topology management policy, kubelet calls each Hint Provider to discover their resource availability. Using this information, the Topology Manager determines if a single NUMA Node affinity is possible. If it is, the pod is admitted to the node. If a single NUMA Node affinity is not possible, the Topology Manager rejects the pod from the node. This results in a pod in a Terminated state with a pod admission failure.
To use Topology Manager, you must configure an allocation policy in the KubeletConfig
custom resource (CR) named cpumanager-enabled
. This file might exist if you have set up CPU Manager. If the file does not exist, you can create the file.
Configure the CPU Manager policy to be static
.
To activate Topology Manager:
Configure the Topology Manager allocation policy in the custom resource.
$ oc edit KubeletConfig cpumanager-enabled
apiVersion: machineconfiguration.openshift.io/v1
kind: KubeletConfig
metadata:
name: cpumanager-enabled
spec:
machineConfigPoolSelector:
matchLabels:
custom-kubelet: cpumanager-enabled
kubeletConfig:
cpuManagerPolicy: static (1)
cpuManagerReconcilePeriod: 5s
topologyManagerPolicy: single-numa-node (2)
1 | This parameter must be static with a lowercase s . |
2 | Specify your selected Topology Manager allocation policy. Here, the policy is single-numa-node .
Acceptable values are: default , best-effort , restricted , single-numa-node . |
The example Pod
specs below help illustrate pod interactions with Topology Manager.
The following pod runs in the BestEffort
QoS class because no resource requests or limits are specified.
spec:
containers:
- name: nginx
image: nginx
The next pod runs in the Burstable
QoS class because requests are less than limits.
spec:
containers:
- name: nginx
image: nginx
resources:
limits:
memory: "200Mi"
requests:
memory: "100Mi"
If the selected policy is anything other than none
, Topology Manager would not consider either of these Pod
specifications.
The last example pod below runs in the Guaranteed QoS class because requests are equal to limits.
spec:
containers:
- name: nginx
image: nginx
resources:
limits:
memory: "200Mi"
cpu: "2"
example.com/device: "1"
requests:
memory: "200Mi"
cpu: "2"
example.com/device: "1"
Topology Manager would consider this pod. The Topology Manager would consult the hint providers, which are CPU Manager and Device Manager, to get topology hints for the pod.
Topology Manager will use this information to store the best topology for this container. In the case of this pod, CPU Manager and Device Manager will use this stored information at the resource allocation stage.
For each compute resource, a container may specify a resource request and limit. Scheduling decisions are made based on the request to ensure that a node has enough capacity available to meet the requested value. If a container specifies limits, but omits requests, the requests are defaulted to the limits. A container is not able to exceed the specified limit on the node.
The enforcement of limits is dependent upon the compute resource type. If a container makes no request or limit, the container is scheduled to a node with no resource guarantees. In practice, the container is able to consume as much of the specified resource as is available with the lowest local priority. In low resource situations, containers that specify no resource requests are given the lowest quality of service.
Scheduling is based on resources requested, while quota and hard limits refer to resource limits, which can be set higher than requested resources. The difference between request and limit determines the level of overcommit; for instance, if a container is given a memory request of 1Gi and a memory limit of 2Gi, it is scheduled based on the 1Gi request being available on the node, but could use up to 2Gi; so it is 200% overcommitted.
The Cluster Resource Override Operator is an admission webhook that allows you to control the level of overcommit and manage container density across all the nodes in your cluster. The Operator controls how nodes in specific projects can exceed defined memory and CPU limits.
You must install the Cluster Resource Override Operator using the OKD console or CLI as shown in the following sections.
During the installation, you create a ClusterResourceOverride
custom resource (CR), where you set the level of overcommit, as shown in the
following example:
apiVersion: operator.autoscaling.openshift.io/v1
kind: ClusterResourceOverride
metadata:
name: cluster (1)
spec:
podResourceOverride:
spec:
memoryRequestToLimitPercent: 50 (2)
cpuRequestToLimitPercent: 25 (3)
limitCPUToMemoryPercent: 200 (4)
# ...
1 | The name must be cluster . |
2 | Optional. If a container memory limit has been specified or defaulted, the memory request is overridden to this percentage of the limit, between 1-100. The default is 50. |
3 | Optional. If a container CPU limit has been specified or defaulted, the CPU request is overridden to this percentage of the limit, between 1-100. The default is 25. |
4 | Optional. If a container memory limit has been specified or defaulted, the CPU limit is overridden to a percentage of the memory limit, if specified. Scaling 1Gi of RAM at 100 percent is equal to 1 CPU core. This is processed prior to overriding the CPU request (if configured). The default is 200. |
The Cluster Resource Override Operator overrides have no effect if limits have not
been set on containers. Create a |
When configured, overrides can be enabled per-project by applying the following label to the Namespace object for each project:
apiVersion: v1
kind: Namespace
metadata:
# ...
labels:
clusterresourceoverrides.admission.autoscaling.openshift.io/enabled: "true"
# ...
The Operator watches for the ClusterResourceOverride
CR and ensures that the ClusterResourceOverride
admission webhook is installed into the same namespace as the operator.
You can use the OKD web console to install the Cluster Resource Override Operator to help control overcommit in your cluster.
The Cluster Resource Override Operator has no effect if limits have not
been set on containers. You must specify default limits for a project using a limitrange
object or configure limits in Pod
specs for the overrides to apply.
To install the Cluster Resource Override Operator using the OKD web console:
In the OKD web console, navigate to Home → Projects
Click Create Project.
Specify clusterresourceoverride-operator
as the name of the project.
Click Create.
Navigate to Operators → OperatorHub.
Choose ClusterResourceOverride Operator from the list of available Operators and click Install.
On the Install Operator page, make sure A specific Namespace on the cluster is selected for Installation Mode.
Make sure clusterresourceoverride-operator is selected for Installed Namespace.
Select an Update Channel and Approval Strategy.
Click Install.
On the Installed Operators page, click ClusterResourceOverride.
On the ClusterResourceOverride Operator details page, click Create ClusterResourceOverride.
On the Create ClusterResourceOverride page, click YAML view and edit the YAML template to set the overcommit values as needed:
apiVersion: operator.autoscaling.openshift.io/v1
kind: ClusterResourceOverride
metadata:
name: cluster (1)
spec:
podResourceOverride:
spec:
memoryRequestToLimitPercent: 50 (2)
cpuRequestToLimitPercent: 25 (3)
limitCPUToMemoryPercent: 200 (4)
# ...
1 | The name must be cluster . |
2 | Optional. Specify the percentage to override the container memory limit, if used, between 1-100. The default is 50. |
3 | Optional. Specify the percentage to override the container CPU limit, if used, between 1-100. The default is 25. |
4 | Optional. Specify the percentage to override the container memory limit, if used. Scaling 1Gi of RAM at 100 percent is equal to 1 CPU core. This is processed prior to overriding the CPU request, if configured. The default is 200. |
Click Create.
Check the current state of the admission webhook by checking the status of the cluster custom resource:
On the ClusterResourceOverride Operator page, click cluster.
On the ClusterResourceOverride Details page, click YAML. The mutatingWebhookConfigurationRef
section appears when the webhook is called.
apiVersion: operator.autoscaling.openshift.io/v1
kind: ClusterResourceOverride
metadata:
annotations:
kubectl.kubernetes.io/last-applied-configuration: |
{"apiVersion":"operator.autoscaling.openshift.io/v1","kind":"ClusterResourceOverride","metadata":{"annotations":{},"name":"cluster"},"spec":{"podResourceOverride":{"spec":{"cpuRequestToLimitPercent":25,"limitCPUToMemoryPercent":200,"memoryRequestToLimitPercent":50}}}}
creationTimestamp: "2019-12-18T22:35:02Z"
generation: 1
name: cluster
resourceVersion: "127622"
selfLink: /apis/operator.autoscaling.openshift.io/v1/clusterresourceoverrides/cluster
uid: 978fc959-1717-4bd1-97d0-ae00ee111e8d
spec:
podResourceOverride:
spec:
cpuRequestToLimitPercent: 25
limitCPUToMemoryPercent: 200
memoryRequestToLimitPercent: 50
status:
# ...
mutatingWebhookConfigurationRef: (1)
apiVersion: admissionregistration.k8s.io/v1
kind: MutatingWebhookConfiguration
name: clusterresourceoverrides.admission.autoscaling.openshift.io
resourceVersion: "127621"
uid: 98b3b8ae-d5ce-462b-8ab5-a729ea8f38f3
# ...
1 | Reference to the ClusterResourceOverride admission webhook. |
You can use the OKD CLI to install the Cluster Resource Override Operator to help control overcommit in your cluster.
The Cluster Resource Override Operator has no effect if limits have not been set on containers. You must specify default limits for a project using a limitrange
object or configure limits in Pod
specs for the overrides to apply.
To install the Cluster Resource Override Operator using the CLI:
Create a namespace for the Cluster Resource Override Operator:
Create a Namespace
object YAML file (for example, cro-namespace.yaml
) for the Cluster Resource Override Operator:
apiVersion: v1
kind: Namespace
metadata:
name: clusterresourceoverride-operator
Create the namespace:
$ oc create -f <file-name>.yaml
For example:
$ oc create -f cro-namespace.yaml
Create an Operator group:
Create an OperatorGroup
object YAML file (for example, cro-og.yaml) for the Cluster Resource Override Operator:
apiVersion: operators.coreos.com/v1
kind: OperatorGroup
metadata:
name: clusterresourceoverride-operator
namespace: clusterresourceoverride-operator
spec:
targetNamespaces:
- clusterresourceoverride-operator
Create the Operator Group:
$ oc create -f <file-name>.yaml
For example:
$ oc create -f cro-og.yaml
Create a subscription:
Create a Subscription
object YAML file (for example, cro-sub.yaml) for the Cluster Resource Override Operator:
apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
name: clusterresourceoverride
namespace: clusterresourceoverride-operator
spec:
channel: "4.13"
name: clusterresourceoverride
source: redhat-operators
sourceNamespace: openshift-marketplace
Create the subscription:
$ oc create -f <file-name>.yaml
For example:
$ oc create -f cro-sub.yaml
Create a ClusterResourceOverride
custom resource (CR) object in the clusterresourceoverride-operator
namespace:
Change to the clusterresourceoverride-operator
namespace.
$ oc project clusterresourceoverride-operator
Create a ClusterResourceOverride
object YAML file (for example, cro-cr.yaml) for the Cluster Resource Override Operator:
apiVersion: operator.autoscaling.openshift.io/v1
kind: ClusterResourceOverride
metadata:
name: cluster (1)
spec:
podResourceOverride:
spec:
memoryRequestToLimitPercent: 50 (2)
cpuRequestToLimitPercent: 25 (3)
limitCPUToMemoryPercent: 200 (4)
1 | The name must be cluster . |
2 | Optional. Specify the percentage to override the container memory limit, if used, between 1-100. The default is 50. |
3 | Optional. Specify the percentage to override the container CPU limit, if used, between 1-100. The default is 25. |
4 | Optional. Specify the percentage to override the container memory limit, if used. Scaling 1Gi of RAM at 100 percent is equal to 1 CPU core. This is processed prior to overriding the CPU request, if configured. The default is 200. |
Create the ClusterResourceOverride
object:
$ oc create -f <file-name>.yaml
For example:
$ oc create -f cro-cr.yaml
Verify the current state of the admission webhook by checking the status of the cluster custom resource.
$ oc get clusterresourceoverride cluster -n clusterresourceoverride-operator -o yaml
The mutatingWebhookConfigurationRef
section appears when the webhook is called.
apiVersion: operator.autoscaling.openshift.io/v1
kind: ClusterResourceOverride
metadata:
annotations:
kubectl.kubernetes.io/last-applied-configuration: |
{"apiVersion":"operator.autoscaling.openshift.io/v1","kind":"ClusterResourceOverride","metadata":{"annotations":{},"name":"cluster"},"spec":{"podResourceOverride":{"spec":{"cpuRequestToLimitPercent":25,"limitCPUToMemoryPercent":200,"memoryRequestToLimitPercent":50}}}}
creationTimestamp: "2019-12-18T22:35:02Z"
generation: 1
name: cluster
resourceVersion: "127622"
selfLink: /apis/operator.autoscaling.openshift.io/v1/clusterresourceoverrides/cluster
uid: 978fc959-1717-4bd1-97d0-ae00ee111e8d
spec:
podResourceOverride:
spec:
cpuRequestToLimitPercent: 25
limitCPUToMemoryPercent: 200
memoryRequestToLimitPercent: 50
status:
# ...
mutatingWebhookConfigurationRef: (1)
apiVersion: admissionregistration.k8s.io/v1
kind: MutatingWebhookConfiguration
name: clusterresourceoverrides.admission.autoscaling.openshift.io
resourceVersion: "127621"
uid: 98b3b8ae-d5ce-462b-8ab5-a729ea8f38f3
# ...
1 | Reference to the ClusterResourceOverride admission webhook. |
The Cluster Resource Override Operator requires a ClusterResourceOverride
custom resource (CR)
and a label for each project where you want the Operator to control overcommit.
The Cluster Resource Override Operator has no effect if limits have not
been set on containers. You must specify default limits for a project using a limitrange
object or configure limits in Pod
specs for the overrides to apply.
To modify cluster-level overcommit:
Edit the ClusterResourceOverride
CR:
apiVersion: operator.autoscaling.openshift.io/v1
kind: ClusterResourceOverride
metadata:
name: cluster
spec:
podResourceOverride:
spec:
memoryRequestToLimitPercent: 50 (1)
cpuRequestToLimitPercent: 25 (2)
limitCPUToMemoryPercent: 200 (3)
# ...
1 | Optional. Specify the percentage to override the container memory limit, if used, between 1-100. The default is 50. |
2 | Optional. Specify the percentage to override the container CPU limit, if used, between 1-100. The default is 25. |
3 | Optional. Specify the percentage to override the container memory limit, if used. Scaling 1Gi of RAM at 100 percent is equal to 1 CPU core. This is processed prior to overriding the CPU request, if configured. The default is 200. |
Ensure the following label has been added to the Namespace object for each project where you want the Cluster Resource Override Operator to control overcommit:
apiVersion: v1
kind: Namespace
metadata:
# ...
labels:
clusterresourceoverrides.admission.autoscaling.openshift.io/enabled: "true" (1)
# ...
1 | Add this label to each project. |
You can use various ways to control overcommit on specific nodes, such as quality of service (QOS) guarantees, CPU limits, or reserve resources. You can also disable overcommit for specific nodes and specific projects.
The node-enforced behavior for compute resources is specific to the resource type.
A container is guaranteed the amount of CPU it requests and is additionally able to consume excess CPU available on the node, up to any limit specified by the container. If multiple containers are attempting to use excess CPU, CPU time is distributed based on the amount of CPU requested by each container.
For example, if one container requested 500m of CPU time and another container requested 250m of CPU time, then any extra CPU time available on the node is distributed among the containers in a 2:1 ratio. If a container specified a limit, it will be throttled not to use more CPU than the specified limit. CPU requests are enforced using the CFS shares support in the Linux kernel. By default, CPU limits are enforced using the CFS quota support in the Linux kernel over a 100ms measuring interval, though this can be disabled.
A container is guaranteed the amount of memory it requests. A container can use more memory than requested, but once it exceeds its requested amount, it could be terminated in a low memory situation on the node. If a container uses less memory than requested, it will not be terminated unless system tasks or daemons need more memory than was accounted for in the node’s resource reservation. If a container specifies a limit on memory, it is immediately terminated if it exceeds the limit amount.
A node is overcommitted when it has a pod scheduled that makes no request, or when the sum of limits across all pods on that node exceeds available machine capacity.
In an overcommitted environment, it is possible that the pods on the node will attempt to use more compute resource than is available at any given point in time. When this occurs, the node must give priority to one pod over another. The facility used to make this decision is referred to as a Quality of Service (QoS) Class.
A pod is designated as one of three QoS classes with decreasing order of priority:
Priority | Class Name | Description |
---|---|---|
1 (highest) |
Guaranteed |
If limits and optionally requests are set (not equal to 0) for all resources and they are equal, then the pod is classified as Guaranteed. |
2 |
Burstable |
If requests and optionally limits are set (not equal to 0) for all resources, and they are not equal, then the pod is classified as Burstable. |
3 (lowest) |
BestEffort |
If requests and limits are not set for any of the resources, then the pod is classified as BestEffort. |
Memory is an incompressible resource, so in low memory situations, containers that have the lowest priority are terminated first:
Guaranteed containers are considered top priority, and are guaranteed to only be terminated if they exceed their limits, or if the system is under memory pressure and there are no lower priority containers that can be evicted.
Burstable containers under system memory pressure are more likely to be terminated once they exceed their requests and no other BestEffort containers exist.
BestEffort containers are treated with the lowest priority. Processes in these containers are first to be terminated if the system runs out of memory.
You can use the qos-reserved
parameter to specify a percentage of memory to be reserved
by a pod in a particular QoS level. This feature attempts to reserve requested resources to exclude pods
from lower OoS classes from using resources requested by pods in higher QoS classes.
OKD uses the qos-reserved
parameter as follows:
A value of qos-reserved=memory=100%
will prevent the Burstable
and BestEffort
QoS classes from consuming memory
that was requested by a higher QoS class. This increases the risk of inducing OOM
on BestEffort
and Burstable
workloads in favor of increasing memory resource guarantees
for Guaranteed
and Burstable
workloads.
A value of qos-reserved=memory=50%
will allow the Burstable
and BestEffort
QoS classes
to consume half of the memory requested by a higher QoS class.
A value of qos-reserved=memory=0%
will allow a Burstable
and BestEffort
QoS classes to consume up to the full node
allocatable amount if available, but increases the risk that a Guaranteed
workload
will not have access to requested memory. This condition effectively disables this feature.
You can disable swap by default on your nodes to preserve quality of service (QOS) guarantees. Otherwise, physical resources on a node can oversubscribe, affecting the resource guarantees the Kubernetes scheduler makes during pod placement.
For example, if two guaranteed pods have reached their memory limit, each container could start using swap memory. Eventually, if there is not enough swap space, processes in the pods can be terminated due to the system being oversubscribed.
Failing to disable swap results in nodes not recognizing that they are experiencing MemoryPressure, resulting in pods not receiving the memory they made in their scheduling request. As a result, additional pods are placed on the node to further increase memory pressure, ultimately increasing your risk of experiencing a system out of memory (OOM) event.
If swap is enabled, any out-of-resource handling eviction thresholds for available memory will not work as expected. Take advantage of out-of-resource handling to allow pods to be evicted from a node when it is under memory pressure, and rescheduled on an alternative node that has no such pressure. |
In an overcommitted environment, it is important to properly configure your node to provide best system behavior.
When the node starts, it ensures that the kernel tunable flags for memory management are set properly. The kernel should never fail memory allocations unless it runs out of physical memory.
To ensure this behavior, OKD configures the kernel to always overcommit
memory by setting the vm.overcommit_memory
parameter to 1
, overriding the
default operating system setting.
OKD also configures the kernel not to panic when it runs out of memory
by setting the vm.panic_on_oom
parameter to 0
. A setting of 0 instructs the
kernel to call oom_killer in an Out of Memory (OOM) condition, which kills
processes based on priority
You can view the current setting by running the following commands on your nodes:
$ sysctl -a |grep commit
#...
vm.overcommit_memory = 0
#...
$ sysctl -a |grep panic
#...
vm.panic_on_oom = 0
#...
The above flags should already be set on nodes, and no further action is required. |
You can also perform the following configurations for each node:
Disable or enforce CPU limits using CPU CFS quotas
Reserve resources for system processes
Reserve memory across quality of service tiers
Nodes by default enforce specified CPU limits using the Completely Fair Scheduler (CFS) quota support in the Linux kernel.
If you disable CPU limit enforcement, it is important to understand the impact on your node:
If a container has a CPU request, the request continues to be enforced by CFS shares in the Linux kernel.
If a container does not have a CPU request, but does have a CPU limit, the CPU request defaults to the specified CPU limit, and is enforced by CFS shares in the Linux kernel.
If a container has both a CPU request and limit, the CPU request is enforced by CFS shares in the Linux kernel, and the CPU limit has no impact on the node.
Obtain the label associated with the static MachineConfigPool
CRD for the type of node you want to configure by entering the following command:
$ oc edit machineconfigpool <name>
For example:
$ oc edit machineconfigpool worker
apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfigPool
metadata:
creationTimestamp: "2022-11-16T15:34:25Z"
generation: 4
labels:
pools.operator.machineconfiguration.openshift.io/worker: "" (1)
name: worker
1 | The label appears under Labels. |
If the label is not present, add a key/value pair such as:
|
Create a custom resource (CR) for your configuration change.
apiVersion: machineconfiguration.openshift.io/v1
kind: KubeletConfig
metadata:
name: disable-cpu-units (1)
spec:
machineConfigPoolSelector:
matchLabels:
pools.operator.machineconfiguration.openshift.io/worker: "" (2)
kubeletConfig:
cpuCfsQuota: false (3)
1 | Assign a name to CR. |
2 | Specify the label from the machine config pool. |
3 | Set the cpuCfsQuota parameter to false . |
Run the following command to create the CR:
$ oc create -f <file_name>.yaml
To provide more reliable scheduling and minimize node resource overcommitment, each node can reserve a portion of its resources for use by system daemons that are required to run on your node for your cluster to function. In particular, it is recommended that you reserve resources for incompressible resources such as memory.
To explicitly reserve resources for non-pod processes, allocate node resources by specifying resources available for scheduling. For more details, see Allocating Resources for Nodes.
To help control overcommit, you can set per-project resource limit ranges, specifying memory and CPU limits and defaults for a project that overcommit cannot exceed.
For information on project-level resource limits, see Additional resources.
Alternatively, you can disable overcommitment for specific projects.
When enabled, overcommitment can be disabled per-project. For example, you can allow infrastructure components to be configured independently of overcommitment.
To disable overcommitment in a project:
Create or edit the namespace object file.
Add the following annotation:
apiVersion: v1
kind: Namespace
metadata:
annotations:
quota.openshift.io/cluster-resource-override-enabled: "false" (1)
# ...
1 | Setting this annotation to false disables overcommit for this namespace. |
Understand and use garbage collection.
Container garbage collection removes terminated containers by using eviction thresholds.
When eviction thresholds are set for garbage collection, the node tries to keep any container for any pod accessible from the API. If the pod has been deleted, the containers will be as well. Containers are preserved as long the pod is not deleted and the eviction threshold is not reached. If the node is under disk pressure, it will remove containers and their logs will no longer be accessible using oc logs
.
eviction-soft - A soft eviction threshold pairs an eviction threshold with a required administrator-specified grace period.
eviction-hard - A hard eviction threshold has no grace period, and if observed, OKD takes immediate action.
The following table lists the eviction thresholds:
Node condition | Eviction signal | Description |
---|---|---|
MemoryPressure |
|
The available memory on the node. |
DiskPressure |
|
The available disk space or inodes on the node root file system, |
For |
If a node is oscillating above and below a soft eviction threshold, but not exceeding its associated grace period, the corresponding node would constantly oscillate between true
and false
. As a consequence, the scheduler could make poor scheduling decisions.
To protect against this oscillation, use the eviction-pressure-transition-period
flag to control how long OKD must wait before transitioning out of a pressure condition. OKD will not set an eviction threshold as being met for the specified pressure condition for the period specified before toggling the condition back to false.
Image garbage collection removes images that are not referenced by any running pods.
OKD determines which images to remove from a node based on the disk usage that is reported by cAdvisor.
The policy for image garbage collection is based on two conditions:
The percent of disk usage (expressed as an integer) which triggers image garbage collection. The default is 85.
The percent of disk usage (expressed as an integer) to which image garbage collection attempts to free. Default is 80.
For image garbage collection, you can modify any of the following variables using a custom resource.
Setting | Description |
---|---|
|
The minimum age for an unused image before the image is removed by garbage collection. The default is 2m. |
|
The percent of disk usage, expressed as an integer, which triggers image garbage collection. The default is 85. |
|
The percent of disk usage, expressed as an integer, to which image garbage collection attempts to free. The default is 80. |
Two lists of images are retrieved in each garbage collector run:
A list of images currently running in at least one pod.
A list of images available on a host.
As new containers are run, new images appear. All images are marked with a time stamp. If the image is running (the first list above) or is newly detected (the second list above), it is marked with the current time. The remaining images are already marked from the previous spins. All images are then sorted by the time stamp.
Once the collection starts, the oldest images get deleted first until the stopping criterion is met.
As an administrator, you can configure how OKD performs garbage collection by creating a kubeletConfig
object for each machine config pool.
OKD supports only one |
You can configure any combination of the following:
Soft eviction for containers
Hard eviction for containers
Eviction for images
Container garbage collection removes terminated containers. Image garbage collection removes images that are not referenced by any running pods.
Obtain the label associated with the static MachineConfigPool
CRD for the type of node you want to configure by entering the following command:
$ oc edit machineconfigpool <name>
For example:
$ oc edit machineconfigpool worker
apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfigPool
metadata:
creationTimestamp: "2022-11-16T15:34:25Z"
generation: 4
labels:
pools.operator.machineconfiguration.openshift.io/worker: "" (1)
name: worker
#...
1 | The label appears under Labels. |
If the label is not present, add a key/value pair such as: $ oc label machineconfigpool worker custom-kubelet=small-pods |
Create a custom resource (CR) for your configuration change.
If there is one file system, or if |
apiVersion: machineconfiguration.openshift.io/v1
kind: KubeletConfig
metadata:
name: worker-kubeconfig (1)
spec:
machineConfigPoolSelector:
matchLabels:
pools.operator.machineconfiguration.openshift.io/worker: "" (2)
kubeletConfig:
evictionSoft: (3)
memory.available: "500Mi" (4)
nodefs.available: "10%"
nodefs.inodesFree: "5%"
imagefs.available: "15%"
imagefs.inodesFree: "10%"
evictionSoftGracePeriod: (5)
memory.available: "1m30s"
nodefs.available: "1m30s"
nodefs.inodesFree: "1m30s"
imagefs.available: "1m30s"
imagefs.inodesFree: "1m30s"
evictionHard: (6)
memory.available: "200Mi"
nodefs.available: "5%"
nodefs.inodesFree: "4%"
imagefs.available: "10%"
imagefs.inodesFree: "5%"
evictionPressureTransitionPeriod: 0s (7)
imageMinimumGCAge: 5m (8)
imageGCHighThresholdPercent: 80 (9)
imageGCLowThresholdPercent: 75 (10)
#...
1 | Name for the object. |
2 | Specify the label from the machine config pool. |
3 | For container garbage collection: Type of eviction: evictionSoft or evictionHard . |
4 | For container garbage collection: Eviction thresholds based on a specific eviction trigger signal. |
5 | For container garbage collection: Grace periods for the soft eviction. This parameter does not apply to eviction-hard . |
6 | For container garbage collection: Eviction thresholds based on a specific eviction trigger signal.
For evictionHard you must specify all of these parameters. If you do not specify all parameters, only the specified parameters are applied and the garbage collection will not function properly. |
7 | For container garbage collection: The duration to wait before transitioning out of an eviction pressure condition. |
8 | For image garbage collection: The minimum age for an unused image before the image is removed by garbage collection. |
9 | For image garbage collection: The percent of disk usage (expressed as an integer) that triggers image garbage collection. |
10 | For image garbage collection: The percent of disk usage (expressed as an integer) that image garbage collection attempts to free. |
Run the following command to create the CR:
$ oc create -f <file_name>.yaml
For example:
$ oc create -f gc-container.yaml
kubeletconfig.machineconfiguration.openshift.io/gc-container created
Verify that garbage collection is active by entering the following command. The Machine Config Pool you specified in the custom resource appears with UPDATING
as 'true` until the change is fully implemented:
$ oc get machineconfigpool
NAME CONFIG UPDATED UPDATING
master rendered-master-546383f80705bd5aeaba93 True False
worker rendered-worker-b4c51bb33ccaae6fc4a6a5 False True
Understand and use the Node Tuning Operator.
The Node Tuning Operator helps you manage node-level tuning by orchestrating the TuneD daemon and achieves low latency performance by using the Performance Profile controller. The majority of high-performance applications require some level of kernel tuning. The Node Tuning Operator provides a unified management interface to users of node-level sysctls and more flexibility to add custom tuning specified by user needs.
The Operator manages the containerized TuneD daemon for OKD as a Kubernetes daemon set. It ensures the custom tuning specification is passed to all containerized TuneD daemons running in the cluster in the format that the daemons understand. The daemons run on all nodes in the cluster, one per node.
Node-level settings applied by the containerized TuneD daemon are rolled back on an event that triggers a profile change or when the containerized TuneD daemon is terminated gracefully by receiving and handling a termination signal.
The Node Tuning Operator uses the Performance Profile controller to implement automatic tuning to achieve low latency performance for OKD applications.
The cluster administrator configures a performance profile to define node-level settings such as the following:
Updating the kernel to kernel-rt.
Choosing CPUs for housekeeping.
Choosing CPUs for running workloads.
Currently, disabling CPU load balancing is not supported by cgroup v2. As a result, you might not get the desired behavior from performance profiles if you have cgroup v2 enabled. Enabling cgroup v2 is not recommended if you are using performance profiles. |
The Node Tuning Operator is part of a standard OKD installation in version 4.1 and later.
In earlier versions of OKD, the Performance Addon Operator was used to implement automatic tuning to achieve low latency performance for OpenShift applications. In OKD 4.11 and later, this functionality is part of the Node Tuning Operator. |
Use this process to access an example Node Tuning Operator specification.
Run the following command to access an example Node Tuning Operator specification:
oc get tuned.tuned.openshift.io/default -o yaml -n openshift-cluster-node-tuning-operator
The default CR is meant for delivering standard node-level tuning for the OKD platform and it can only be modified to set the Operator Management state. Any other custom changes to the default CR will be overwritten by the Operator. For custom tuning, create your own Tuned CRs. Newly created CRs will be combined with the default CR and custom tuning applied to OKD nodes based on node or pod labels and profile priorities.
While in certain situations the support for pod labels can be a convenient way of automatically delivering required tuning, this practice is discouraged and strongly advised against, especially in large-scale clusters. The default Tuned CR ships without pod label matching. If a custom profile is created with pod label matching, then the functionality will be enabled at that time. The pod label functionality will be deprecated in future versions of the Node Tuning Operator. |
The custom resource (CR) for the Operator has two major sections. The first section, profile:
, is a list of TuneD profiles and their names. The second, recommend:
, defines the profile selection logic.
Multiple custom tuning specifications can co-exist as multiple CRs in the Operator’s namespace. The existence of new CRs or the deletion of old CRs is detected by the Operator. All existing custom tuning specifications are merged and appropriate objects for the containerized TuneD daemons are updated.
Management state
The Operator Management state is set by adjusting the default Tuned CR. By default, the Operator is in the Managed state and the spec.managementState
field is not present in the default Tuned CR. Valid values for the Operator Management state are as follows:
Managed: the Operator will update its operands as configuration resources are updated
Unmanaged: the Operator will ignore changes to the configuration resources
Removed: the Operator will remove its operands and resources the Operator provisioned
Profile data
The profile:
section lists TuneD profiles and their names.
profile:
- name: tuned_profile_1
data: |
# TuneD profile specification
[main]
summary=Description of tuned_profile_1 profile
[sysctl]
net.ipv4.ip_forward=1
# ... other sysctl's or other TuneD daemon plugins supported by the containerized TuneD
# ...
- name: tuned_profile_n
data: |
# TuneD profile specification
[main]
summary=Description of tuned_profile_n profile
# tuned_profile_n profile settings
Recommended profiles
The profile:
selection logic is defined by the recommend:
section of the CR. The recommend:
section is a list of items to recommend the profiles based on a selection criteria.
recommend:
<recommend-item-1>
# ...
<recommend-item-n>
The individual items of the list:
- machineConfigLabels: (1)
<mcLabels> (2)
match: (3)
<match> (4)
priority: <priority> (5)
profile: <tuned_profile_name> (6)
operand: (7)
debug: <bool> (8)
tunedConfig:
reapply_sysctl: <bool> (9)
1 | Optional. |
2 | A dictionary of key/value MachineConfig labels. The keys must be unique. |
3 | If omitted, profile match is assumed unless a profile with a higher priority matches first or machineConfigLabels is set. |
4 | An optional list. |
5 | Profile ordering priority. Lower numbers mean higher priority (0 is the highest priority). |
6 | A TuneD profile to apply on a match. For example tuned_profile_1 . |
7 | Optional operand configuration. |
8 | Turn debugging on or off for the TuneD daemon. Options are true for on or false for off. The default is false . |
9 | Turn reapply_sysctl functionality on or off for the TuneD daemon. Options are true for on and false for off. |
<match>
is an optional list recursively defined as follows:
- label: <label_name> (1)
value: <label_value> (2)
type: <label_type> (3)
<match> (4)
1 | Node or pod label name. |
2 | Optional node or pod label value. If omitted, the presence of <label_name> is enough to match. |
3 | Optional object type (node or pod ). If omitted, node is assumed. |
4 | An optional <match> list. |
If <match>
is not omitted, all nested <match>
sections must also evaluate to true
. Otherwise, false
is assumed and the profile with the respective <match>
section will not be applied or recommended. Therefore, the nesting (child <match>
sections) works as logical AND operator. Conversely, if any item of the <match>
list matches, the entire <match>
list evaluates to true
. Therefore, the list acts as logical OR operator.
If machineConfigLabels
is defined, machine config pool based matching is turned on for the given recommend:
list item. <mcLabels>
specifies the labels for a machine config. The machine config is created automatically to apply host settings, such as kernel boot parameters, for the profile <tuned_profile_name>
. This involves finding all machine config pools with machine config selector matching <mcLabels>
and setting the profile <tuned_profile_name>
on all nodes that are assigned the found machine config pools. To target nodes that have both master and worker roles, you must use the master role.
The list items match
and machineConfigLabels
are connected by the logical OR operator. The match
item is evaluated first in a short-circuit manner. Therefore, if it evaluates to true
, the machineConfigLabels
item is not considered.
When using machine config pool based matching, it is advised to group nodes with the same hardware configuration into the same machine config pool. Not following this practice might result in TuneD operands calculating conflicting kernel parameters for two or more nodes sharing the same machine config pool. |
- match:
- label: tuned.openshift.io/elasticsearch
match:
- label: node-role.kubernetes.io/master
- label: node-role.kubernetes.io/infra
type: pod
priority: 10
profile: openshift-control-plane-es
- match:
- label: node-role.kubernetes.io/master
- label: node-role.kubernetes.io/infra
priority: 20
profile: openshift-control-plane
- priority: 30
profile: openshift-node
The CR above is translated for the containerized TuneD daemon into its recommend.conf
file based on the profile priorities. The profile with the highest priority (10
) is openshift-control-plane-es
and, therefore, it is considered first. The containerized TuneD daemon running on a given node looks to see if there is a pod running on the same node with the tuned.openshift.io/elasticsearch
label set. If not, the entire <match>
section evaluates as false
. If there is such a pod with the label, in order for the <match>
section to evaluate to true
, the node label also needs to be node-role.kubernetes.io/master
or node-role.kubernetes.io/infra
.
If the labels for the profile with priority 10
matched, openshift-control-plane-es
profile is applied and no other profile is considered. If the node/pod label combination did not match, the second highest priority profile (openshift-control-plane
) is considered. This profile is applied if the containerized TuneD pod runs on a node with labels node-role.kubernetes.io/master
or node-role.kubernetes.io/infra
.
Finally, the profile openshift-node
has the lowest priority of 30
. It lacks the <match>
section and, therefore, will always match. It acts as a profile catch-all to set openshift-node
profile, if no other profile with higher priority matches on a given node.
apiVersion: tuned.openshift.io/v1
kind: Tuned
metadata:
name: openshift-node-custom
namespace: openshift-cluster-node-tuning-operator
spec:
profile:
- data: |
[main]
summary=Custom OpenShift node profile with an additional kernel parameter
include=openshift-node
[bootloader]
cmdline_openshift_node_custom=+skew_tick=1
name: openshift-node-custom
recommend:
- machineConfigLabels:
machineconfiguration.openshift.io/role: "worker-custom"
priority: 20
profile: openshift-node-custom
To minimize node reboots, label the target nodes with a label the machine config pool’s node selector will match, then create the Tuned CR above and finally create the custom machine config pool itself.
Cloud provider-specific TuneD profiles
With this functionality, all Cloud provider-specific nodes can conveniently be assigned a TuneD profile specifically tailored to a given Cloud provider on a OKD cluster. This can be accomplished without adding additional node labels or grouping nodes into machine config pools.
This functionality takes advantage of spec.providerID
node object values in the form of <cloud-provider>://<cloud-provider-specific-id>
and writes the file /var/lib/tuned/provider
with the value <cloud-provider>
in NTO operand containers. The content of this file is then used by TuneD to load provider-<cloud-provider>
profile if such profile exists.
The openshift
profile that both openshift-control-plane
and openshift-node
profiles inherit settings from is now updated to use this functionality through the use of conditional profile loading. Neither NTO nor TuneD currently include any Cloud provider-specific profiles. However, it is possible to create a custom profile provider-<cloud-provider>
that will be applied to all Cloud provider-specific cluster nodes.
apiVersion: tuned.openshift.io/v1
kind: Tuned
metadata:
name: provider-gce
namespace: openshift-cluster-node-tuning-operator
spec:
profile:
- data: |
[main]
summary=GCE Cloud provider-specific profile
# Your tuning for GCE Cloud provider goes here.
name: provider-gce
Due to profile inheritance, any setting specified in the |
The following are the default profiles set on a cluster.
apiVersion: tuned.openshift.io/v1
kind: Tuned
metadata:
name: default
namespace: openshift-cluster-node-tuning-operator
spec:
profile:
- data: |
[main]
summary=Optimize systems running OpenShift (provider specific parent profile)
include=-provider-${f:exec:cat:/var/lib/tuned/provider},openshift
name: openshift
recommend:
- profile: openshift-control-plane
priority: 30
match:
- label: node-role.kubernetes.io/master
- label: node-role.kubernetes.io/infra
- profile: openshift-node
priority: 40
Starting with OKD 4.9, all OpenShift TuneD profiles are shipped with
the TuneD package. You can use the oc exec
command to view the contents of these profiles:
$ oc exec $tuned_pod -n openshift-cluster-node-tuning-operator -- find /usr/lib/tuned/openshift{,-control-plane,-node} -name tuned.conf -exec grep -H ^ {} \;
Excluding the [main]
section, the following TuneD plugins are supported when
using custom profiles defined in the profile:
section of the Tuned CR:
audio
cpu
disk
eeepc_she
modules
mounts
net
scheduler
scsi_host
selinux
sysctl
sysfs
usb
video
vm
bootloader
There is some dynamic tuning functionality provided by some of these plugins that is not supported. The following TuneD plugins are currently not supported:
script
systemd
The TuneD bootloader plugin only supports Fedora CoreOS (FCOS) worker nodes. |
Two parameters control the maximum number of pods that can be scheduled to a node: podsPerCore
and maxPods
. If you use both options, the lower of the two limits the number of pods on a node.
For example, if podsPerCore
is set to 10
on a node with 4 processor cores, the maximum number of pods allowed on the node will be 40.
Obtain the label associated with the static MachineConfigPool
CRD for the type of node you want to configure by entering the following command:
$ oc edit machineconfigpool <name>
For example:
$ oc edit machineconfigpool worker
apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfigPool
metadata:
creationTimestamp: "2022-11-16T15:34:25Z"
generation: 4
labels:
pools.operator.machineconfiguration.openshift.io/worker: "" (1)
name: worker
#...
1 | The label appears under Labels. |
If the label is not present, add a key/value pair such as: $ oc label machineconfigpool worker custom-kubelet=small-pods |
Create a custom resource (CR) for your configuration change.
max-pods
CRapiVersion: machineconfiguration.openshift.io/v1
kind: KubeletConfig
metadata:
name: set-max-pods (1)
spec:
machineConfigPoolSelector:
matchLabels:
pools.operator.machineconfiguration.openshift.io/worker: "" (2)
kubeletConfig:
podsPerCore: 10 (3)
maxPods: 250 (4)
#...
1 | Assign a name to CR. |
2 | Specify the label from the machine config pool. |
3 | Specify the number of pods the node can run based on the number of processor cores on the node. |
4 | Specify the number of pods the node can run to a fixed value, regardless of the properties of the node. |
Setting |
In the above example, the default value for podsPerCore
is 10
and the default value for maxPods
is 250
. This means that unless the node has 25 cores or more, by default, podsPerCore
will be the limiting factor.
Run the following command to create the CR:
$ oc create -f <file_name>.yaml
List the MachineConfigPool
CRDs to see if the change is applied. The UPDATING
column reports True
if the change is picked up by the Machine Config Controller:
$ oc get machineconfigpools
NAME CONFIG UPDATED UPDATING DEGRADED
master master-9cc2c72f205e103bb534 False False False
worker worker-8cecd1236b33ee3f8a5e False True False
Once the change is complete, the UPDATED
column reports True
.
$ oc get machineconfigpools
NAME CONFIG UPDATED UPDATING DEGRADED
master master-9cc2c72f205e103bb534 False True False
worker worker-8cecd1236b33ee3f8a5e True False False