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AWS: Update documentation
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cluster-autoscaler/cloudprovider/aws/README.md
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@ -1,14 +1,22 @@
# Cluster Autoscaler on AWS
The cluster autoscaler on AWS scales worker nodes within any specified autoscaling group. It will run as a `Deployment` in your cluster. This README covers the steps required to configure and run the cluster autoscaler.
On AWS, Cluster Autoscaler utilizes Amazon EC2 Auto Scaling Groups to manage node
groups. Cluster Autoscaler typically runs as a `Deployment` in your cluster.
## Kubernetes Version
Cluster autoscaler must run on v1.3.0 or greater.
## Requirements
Cluster Autoscaler requires Kubernetes v1.3.0 or greater.
## Permissions
The pod running the cluster autoscaler will need access to certain resources and actions. If using AWS EKS it is recommend to attach the IAM policy to the cluster austoscaler pod using [IAM roles for Service Accounts](https://docs.aws.amazon.com/eks/latest/userguide/iam-roles-for-service-accounts.html). For non-EKS kubernetes clusters attaching the IAM policy to the NodeGroup is recommended instead of using AWS credentials directly unless you have special requirements.
Cluster Autoscaler requires the ability to examine and modify EC2 Auto Scaling
Groups. We recommend using [IAM roles for Service
Accounts](https://docs.aws.amazon.com/eks/latest/userguide/iam-roles-for-service-accounts.html)
to associate the Service Account that the Cluster Autoscaler Deployment runs as
with an IAM role that is able to perform these functions. If you are unable to
use IAM Roles for Service Accounts, you may associate an IAM service role with
the EC2 instance on which the Cluster Autoscaler pod runs.
### IAM Policy
The following policy provides the minimum privileges necessary for Cluster Autoscaler to run:
### Attach IAM policy to Service Account
A minimum IAM policy would look like:
```json
{
"Version": "2012-10-17",
@ -22,26 +30,35 @@ A minimum IAM policy would look like:
"autoscaling:SetDesiredCapacity",
"autoscaling:TerminateInstanceInAutoScalingGroup"
],
"Resource": "*"
"Resource": ["*"]
}
]
}
```
If you'd like to scale node groups from 0, an
`autoscaling:DescribeLaunchConfigurations` or
`ec2:DescribeLaunchTemplateVersions` permission is required depending on if you
made your ASG with Launch Configuration or Launch Template.
If you'd like the cluster autoscaler to [automatically
discover](#auto-discovery-setup) EC2 AutoScalingGroups, the
`autoscaling:DescribeTags` permission is also required.
If you'd like Cluster Autoscaler to [automatically
discover](#auto-discovery-setup) EC2 Auto Scaling Groups **(recommended)**, add
`autoscaling:DescribeTags` to the `Action` list. Also add
`autoscaling:DescribeLaunchConfigurations` (if you created your ASG using a
Launch Configuration) and/or `ec2:DescribeLaunchTemplateVersions` (if you
created your ASG using a Launch Template) to the `Action` list.
**NOTE**: You can restrict the target resources for the autoscaling actions by
specifying autoscaling group ARNS. More information can be found
If you prefer, you can restrict the target resources for the autoscaling actions
by specifying Auto Scaling Group ARNs in the `Resource` list of the policy. More
information can be found
[here](https://docs.aws.amazon.com/autoscaling/latest/userguide/control-access-using-iam.html#policy-auto-scaling-resources).
### Using AWS Credentials
For on premise users wishing to scale out to AWS, the above approach of attaching policy to a nodegroup role won't work. Instead, you can create an aws secret manually and add following environment variables to cluster-autoscaler deployment manifest. Cluster autoscaler will use credential to authenticate and authorize itself. Please make sure your role has above permissions.
**NOTE** The following is not recommended for Kubernetes clusters running on
AWS. If you are using Amazon EKS, consider using [IAM roles for Service
Accounts](https://docs.aws.amazon.com/eks/latest/userguide/iam-roles-for-service-accounts.html)
instead.
For on-premise clusters, you may create an IAM user subject to the above policy
and provide the IAM credentials as environment variables in the Cluster
Autoscaler deployment manifest. Cluster Autoscaler will use these credentials to
authenticate and authorize itself.
```yaml
apiVersion: v1
@ -53,7 +70,9 @@ data:
aws_access_key_id: BASE64_OF_YOUR_AWS_ACCESS_KEY_ID
aws_secret_access_key: BASE64_OF_YOUR_AWS_SECRET_ACCESS_KEY
```
Please check [guidance](https://kubernetes.io/docs/concepts/configuration/secret/#creating-a-secret-manually) for creating a secret manually.
Please refer to the [relevant Kubernetes
documentation](https://kubernetes.io/docs/concepts/configuration/secret/#creating-a-secret-manually)
for creating a secret manually.
```yaml
env:
@ -71,10 +90,110 @@ env:
value: YOUR_AWS_REGION
```
## Deployment Specification
Auto-Discovery Setup is always preferred option to avoid multiple, potentially different configuration for min/max values. If you want to adjust minimum and maximum size of the group, please adjust size on ASG directly, CA will fetch latest change when talking to ASG.
## Auto-Discovery Setup
If you use one or multiple ASG setup, the min/max configuration change in CA will not make the corresponding change to ASG. Please make sure CA min/max values are within the boundary of ASG minSize and maxSize.
Auto-Discovery Setup is the preferred method to configure Cluster Autoscaler.
To enable this, provide the `--node-group-auto-discovery` flag as an argument
whose value is a list of tag keys that should be looked for. For example,
`--node-group-auto-discovery=asg:tag=k8s.io/cluster-autoscaler/enabled,k8s.io/cluster-autoscaler/<cluster-name>`
will find the ASGs where those tag keys _exist_. It does not matter what value
the tags have.
Example deployment:
```
kubectl apply -f examples/cluster-autoscaler-autodiscover.yaml
```
Cluster Autoscaler will respect the minimum and maximum values of each Auto
Scaling Group. It will only adjust the desired value.
Each Auto Scaling Group should be composed of instance types that provide
approximately equal capacity. For example, ASG "xlarge" could be composed of
m5a.xlarge, m4.xlarge, m5.xlarge, and m5d.xlarge instance types, because each of
those provide 4 vCPUs and 16GiB RAM. Separately, ASG "2xlarge" could be
composed of m5a.2xlarge, m4.2xlarge, m5.2xlarge, and m5d.2xlarge instance
types, because each of those provide 8 vCPUs and 32GiB RAM.
Cluster Autoscaler will attempt to determine the CPU, memory, and GPU resources
provided by an Auto Scaling Group based on the instance type specified in its
Launch Configuration or Launch Template. It will also examine any overrides
provided in an ASG's Mixed Instances Policy. If any such overrides are found,
only the first instance type found will be used. See [Using Mixed Instances
Policies and Spot Instances](#Using-Mixed-Instances-Policies-and-Spot-Instances)
for details.
From version 1.14, Cluster Autoscaler can also determine the resources provided
by each Auto Scaling Group via tags. The tag is of the format
`k8s.io/cluster-autoscaler/node-template/resources/<resource-name>`.
`<resource-name>` is the name of the resource, such as `ephemeral-storage`. The
value of each tag specifies the amount of resource provided. The units are
identical to the units used in the `resources` field of a Pod specification.
Example tags:
* `k8s.io/cluster-autoscaler/node-template/resources/ephemeral-storage`: `100G`
You may also provide additional hints to Cluster Autoscaler that the nodes will
be labeled or tainted when they join the cluster, such as:
* `k8s.io/cluster-autoscaler/node-template/label/foo`: `bar`
* `k8s.io/cluster-autoscaler/node-template/taint/dedicated`: `NoSchedule`
**NOTE:** It is your responsibility to ensure such labels and/or taints are
applied via the node's kubelet configuration at startup.
Recommendations:
* It is recommended to use a second tag like
`k8s.io/cluster-autoscaler/<cluster-name>` when
`k8s.io/cluster-autoscaler/enabled` is used across many clusters to prevent
ASGs from different clusters recognized as the node groups.
* To prevent conflicts, do not provide a `--nodes` argument if
`--node-group-auto-discovery` is specified.
* Be sure to add `autoscaling:DescribeLaunchConfigurations` or
`ec2:DescribeLaunchTemplateVersions` to the `Action` list of the IAM Policy
used by Cluster Autoscaler, depending on whether your ASG utilizes Launch
Configurations or Launch Templates.
* If Cluster Autoscaler adds a node to the cluster, and the node has taints applied
when it joins the cluster that Cluster Autoscaler was unaware of (because the tag
wasn't supplied), this can lead to significant confusion and misbehavior.
### Special note on GPU instances
The device plugin on nodes that provides GPU resources can take some time to
advertise the GPU resource to the cluster. This may cause Cluster Autoscaler to
unnecessarily scale out multiple times.
To avoid this, you can configure `kubelet` on your GPU nodes to label the node
before it joins the cluster by passing it the `--node-labels` flag. The label
format is as follows:
* Cluster Autoscaler < 1.15: `cloud.google.com/gke-accelerator=<gpu-type>`
* Cluster Autoscaler >= 1.15: `k8s.amazonaws.com/accelerator=<gpu-type>`
`<gpu-type>` varies by instance type. On P2 instances, for example, the
value is `nvidia-tesla-k80`.
## Manual configuration
Cluster Autoscaler can also be configured manually if you wish by passing the
`--nodes` argument at startup. The format of the argument is
`--nodes=<min>:<max>:<asg-name>`, where `<min>` is the minimum number of nodes,
`<max>` is the maximum number of nodes, and `<asg-name>` is the Auto Scaling
Group name.
You can pass multiple `--nodes` arguments if you have multiple Auto Scaling Groups
you want Cluster Autoscaler to use.
**NOTES**:
* Both `<min>` and `<max>` must be within the range of the minimum and maximum
instance counts specified by the Auto Scaling group.
* When manual configuration is used, all Auto Scaling groups must use EC2
instance types that provide equal CPU and memory capacity.
Examples:
### One ASG Setup (min: 1, max: 10, ASG Name: k8s-worker-asg-1)
```
@ -86,138 +205,43 @@ kubectl apply -f examples/cluster-autoscaler-one-asg.yaml
kubectl apply -f examples/cluster-autoscaler-multi-asg.yaml
```
### Master Node Setup
## Master Node Setup
To run a CA pod in master node - CA deployment should tolerate the master `taint` and `nodeSelector` should be used to schedule the pods in master node.
Please replace `{{ node_asg_min }}`, `{{ node_asg_max }}` and `{{ name }}` with your ASG setting in the yaml file.
**NOTE**: This setup is not compatible with Amazon EKS.
To run a CA pod in master node - CA deployment should tolerate the master
`taint` and `nodeSelector` should be used to schedule the pods in master node.
Please replace `{{ node_asg_min }}`, `{{ node_asg_max }}` and `{{ name }}` with
your ASG setting in the yaml file.
```
kubectl apply -f examples/cluster-autoscaler-run-on-master.yaml
```
### Auto-Discovery Setup
## Using Mixed Instances Policies and Spot Instances
To run a cluster-autoscaler which auto-discovers ASGs with nodes use the `--node-group-auto-discovery` flag. For example, `--node-group-auto-discovery=asg:tag=k8s.io/cluster-autoscaler/enabled,k8s.io/cluster-autoscaler/<YOUR CLUSTER NAME>` will find the ASGs where those tag keys
_exist_. It does not matter what value the tags have.
**NOTE:** The minimum version of cluster autoscaler to support MixedInstancePolicy is v1.14.x.
Note that:
If your workloads can tolerate interruption, consider taking advantage of Spot
Instances for a lower price point. To enable diversity among On Demand and Spot
Instances, as well as specify multiple EC2 instance types in order to tap into
multiple Spot capacity pools, use a [mixed instances
policy](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-autoscaling-autoscalinggroup-mixedinstancespolicy.html)
on your ASG. Note that the instance types should have the same amount of RAM and
number of CPU cores, since this is fundamental to CA's scaling calculations.
Using mismatched instances types can produce unintended results. See an example
below.
* It is recommended to use a second tag like `k8s.io/cluster-autoscaler/<YOUR CLUSTER NAME>` when `k8s.io/cluster-autoscaler/enabled` is used across many clusters to prevent ASGs from different clusters recognized as the node groups
* There are no `--nodes` flags passed to cluster-autoscaler because the node groups are automatically discovered by tags
* No min/max values are provided when using Auto-Discovery, cluster-autoscaler will respect the current min and max values of the ASG being targeted, and it will adjust only the "desired" value.
```
kubectl apply -f examples/cluster-autoscaler-autodiscover.yaml
```
## Scaling a node group to 0
From CA 0.6.1 - it is possible to scale a node group to 0 (and obviously from 0), assuming that all scale-down conditions are met.
If you are using `nodeSelector` you need to tag the ASG with a node-template key `"k8s.io/cluster-autoscaler/node-template/label/"` and `"k8s.io/cluster-autoscaler/node-template/taint/"` if you are using taints.
If your pods request resources other than `cpu` and `memory`, you need to tag ASG with key `k8s.io/cluster-autoscaler/node-template/resources/`.
For example for a node label of `foo=bar` you would tag the ASG with:
```json
{
"ResourceType": "auto-scaling-group",
"ResourceId": "foo.example.com",
"PropagateAtLaunch": true,
"Value": "bar",
"Key": "k8s.io/cluster-autoscaler/node-template/label/foo"
}
```
And for a taint of `"dedicated": "foo:NoSchedule"` you would tag the ASG with:
```json
{
"ResourceType": "auto-scaling-group",
"ResourceId": "foo.example.com",
"PropagateAtLaunch": true,
"Value": "foo:NoSchedule",
"Key": "k8s.io/cluster-autoscaler/node-template/taint/dedicated"
}
```
If you request other resources on the node, like `vpc.amazonaws.com/PrivateIPv4Address` for Windows nodes, `ephemeral-storage`, etc, you would tag ASG with
```json
{
"ResourceType": "auto-scaling-group",
"ResourceId": "foo.example.com",
"PropagateAtLaunch": true,
"Value": "2",
"Key": "k8s.io/cluster-autoscaler/node-template/resources/vpc.amazonaws.com/PrivateIPv4Address"
}
```
> Note: This is only supported in CA 1.14.x and above
If you'd like to scale node groups from 0, an `autoscaling:DescribeLaunchConfigurations` or `ec2:DescribeLaunchTemplateVersions` permission is required depending on if you made your ASG with Launch Configuration or Launch Template:
```json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"autoscaling:DescribeAutoScalingGroups",
"autoscaling:DescribeAutoScalingInstances",
"autoscaling:DescribeTags",
"autoscaling:DescribeLaunchConfigurations",
"autoscaling:SetDesiredCapacity",
"autoscaling:TerminateInstanceInAutoScalingGroup",
"ec2:DescribeLaunchTemplateVersions"
],
"Resource": "*"
}
]
}
```
### Gotchas
* Without these tags, when the cluster autoscaler needs to increase the number of nodes, if a node group creates nodes with taints that the pending pod does not tolerate then the cluster autoscaler will only learn about this after the node has been created and it sees that it is tainted. From this point on this information will be cached and subsequent scaling operations will take this into account, but it means that the behaviour of the cluster autoscaler differs between the first and subsequent scale up requests and can lead to confusion.
* The device plugin on nodes which provide GPU resources take a little while to advertise the GPU resource to the APIServer so the AutoScaler may unnecessarily scale up again. See the guidance below for how to avoid this
## GPU Node Groups
If you launch a pod that requires a GPU in it's resource requirements then you must add the following node label to the node (via the kubelet arguments for example)
### Cluster AutoScaler Version < 1.15.x
```bash
--node-labels=cloud.google.com/gke-accelerator=<GPU TYPE YOU ARE USING>
```
E.g. on an AWS P2.X instance
```bash
--kubelet-extra-args '--node-labels=cloud.google.com/gke-accelerator=nvidia-tesla-k80'
```
### Cluster AutoScaler Version >= 1.15.x
```bash
--node-labels=k8s.amazonaws.com/accelerator=<GPU TYPE YOU ARE USING>
```
E.g. on an AWS P2.X instance
```bash
--kubelet-extra-args '--node-labels=k8s.amazonaws.com/accelerator=nvidia-tesla-k80'
```
This is because the GPU resource does not become available immediately after the instance is ready and so without this label, the cluster autoscaler will think that no suitable GPU resource is available and add an additional node.
## Using AutoScalingGroup MixedInstancesPolicy
> Note: The minimum version of cluster autoscaler to support MixedInstancePolicy is v1.14.x.
If your workloads can tolerate interruption, consider taking advantage of Spot Instances for a lower price point. To enable diversity among On Demand and Spot Instances, as well as specify multiple EC2 instance types in order to tap into multiple Spot capacity pools, use a [mixed instances policy](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-autoscaling-autoscalinggroup-mixedinstancespolicy.html) on your ASG. Note that the instance types should have the same amount of RAM and number of CPU cores, since this is fundamental to CA's scaling calculations. Using mismatched instances types can produce unintended results. See an example below.
Additionally, there are other factors which affect scaling, such as node labels. If you are currently using `nodeSelector` with the [beta.kubernetes.io/instance-type](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#interlude-built-in-node-labels) label, you will need to apply a common propagating label to the ASG and use that instead, since the instance-type label can no longer be relied upon. One may also use auto-generated tags such as `aws:cloudformation:stack-name` for this purpose. [Node affinity and anti-affinity](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#affinity-and-anti-affinity) are not affected in the same way, since these selectors natively accept multiple values; one must add all the configured instances types to the list of values, for example:
Additionally, there are other factors which affect scaling, such as node labels.
If you are currently using `nodeSelector` with the
[beta.kubernetes.io/instance-type](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#interlude-built-in-node-labels)
label, you will need to apply a common propagating label to the ASG and use that
instead, since the instance-type label can no longer be relied upon. One may
also use auto-generated tags such as `aws:cloudformation:stack-name` for this
purpose. [Node affinity and
anti-affinity](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#affinity-and-anti-affinity)
are not affected in the same way, since these selectors natively accept multiple
values; one must add all the configured instances types to the list of values,
for example:
```yaml
spec:
@ -241,30 +265,89 @@ spec:
### Example usage:
* Create a [Launch Template](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-autoscaling-autoscalinggroup-launchtemplate.html) (LT) with an instance type, for example, r5.2xlarge. Consider this the 'base' instance type. Do not define any spot purchase options here.
* Create a [Launch
Template](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-autoscaling-autoscalinggroup-launchtemplate.html)
(LT) with an instance type, for example, r5.2xlarge. Consider this the 'base'
instance type. Do not define any spot purchase options here.
* Create an ASG with a MixedInstancesPolicy that refers to the newly-created LT.
* Set LaunchTemplateOverrides to include the 'base' instance type r5.2xlarge and suitable alternatives, e.g. r5d.2xlarge, i3.2xlarge, r5a.2xlarge and r5ad.2xlarge. Differing processor types and speeds should be evaluated depending on your use-case(s).
* Set [InstancesDistribution](https://docs.aws.amazon.com/autoscaling/ec2/APIReference/API_InstancesDistribution.html) according to your needs.
* See [Allocation Strategies](https://docs.aws.amazon.com/autoscaling/ec2/userguide/asg-purchase-options.html#asg-allocation-strategies) for information about how the ASG fulfils capacity from the specified instance types. It is recommended to use the capacity-optimized allocation strategy, which will automatically launch Spot Instances into the most available pools by looking at real-time capacity data and.
* For the same workload or for the generic capacity in your cluster, you can also create more node groups with a vCPU/Mem ratio that is a good fit for your workloads, but from different instance sizes. For example:
Node group 1: m5.xlarge, m5a.xlarge, m5d.xlarge, m5ad.xlarge, m4.xlarge.
Node group 2: m5.2xlarge, m5a.2xlarge, m5d.2xlarge, m5ad.2xlarge, m4.2xlarge.
This approach increases the chance of achieving your desired scale at the lowest cost by tapping into many Spot capacity pools.
* Set LaunchTemplateOverrides to include the 'base' instance type r5.2xlarge and
suitable alternatives, e.g. r5d.2xlarge, i3.2xlarge, r5a.2xlarge and
r5ad.2xlarge. Differing processor types and speeds should be evaluated
depending on your use-case(s).
* Set
[InstancesDistribution](https://docs.aws.amazon.com/autoscaling/ec2/APIReference/API_InstancesDistribution.html)
according to your needs.
* See [Allocation
Strategies](https://docs.aws.amazon.com/autoscaling/ec2/userguide/asg-purchase-options.html#asg-allocation-strategies)
for information about how the ASG fulfills capacity from the specified instance
types. It is recommended to use the capacity-optimized allocation strategy,
which will automatically launch Spot Instances into the most available pools
by looking at real-time capacity data and.
* For the same workload or for the generic capacity in your cluster, you can
also create more node groups with a vCPU/Mem ratio that is a good fit for your
workloads, but from different instance sizes. For example: Node group 1:
m5.xlarge, m5a.xlarge, m5d.xlarge, m5ad.xlarge, m4.xlarge. Node group 2:
m5.2xlarge, m5a.2xlarge, m5d.2xlarge, m5ad.2xlarge, m4.2xlarge. This approach
increases the chance of achieving your desired scale at the lowest cost by
tapping into many Spot capacity pools.
See CloudFormation example [here](MixedInstancePolicy.md).
## Use Static Instance List
The set of the latest supported EC2 instance types will be fetched by the CA at run time. You can find all the available instance types in the CA logs.
If your network access is restricted such that fetching this set is infeasible, you can specify the command-line flag `--aws-use-static-instance-list=true` to switch the CA back to its original use of a statically defined set.
The set of the latest supported EC2 instance types will be fetched by the CA at
run time. You can find all the available instance types in the CA logs. If your
network access is restricted such that fetching this set is infeasible, you can
specify the command-line flag `--aws-use-static-instance-list=true` to switch
the CA back to its original use of a statically defined set.
To refresh static list, please run `go run ec2_instance_types/gen.go` under `cluster-autoscaler/cloudprovider/aws/` and update `staticListLastUpdateTime` in `aws_util.go`
To refresh static list, please run `go run ec2_instance_types/gen.go` under
`cluster-autoscaler/cloudprovider/aws/` and update `staticListLastUpdateTime` in
`aws_util.go`
## Common Notes and Gotchas:
- The `/etc/ssl/certs/ca-bundle.crt` should exist by default on ec2 instance in your EKS cluster. If you use other cluster privision tools like [kops](https://github.com/kubernetes/kops) with different operating systems other than Amazon Linux 2, please use `/etc/ssl/certs/ca-certificates.crt` or correct path on your host instead for the volume hostPath in your cluster autoscaler manifest.
- If you’re using Persistent Volumes, your deployment needs to run in the same AZ as where the EBS volume is, otherwise the pod scheduling could fail if it is scheduled in a different AZ and cannot find the EBS volume. To overcome this, either use a single AZ ASG for this use case, or an ASG-per-AZ while enabling [--balance-similar-node-groups](../../FAQ.md#im-running-cluster-with-nodes-in-multiple-zones-for-ha-purposes-is-that-supported-by-cluster-autoscaler). Alternately, and depending on your use-case, you might be able to switch from using EBS to using shared storage that is available across AZs (for each pod in its respective AZ). Consider AWS services like Amazon EFS or Amazon FSx for Lustre.
- On creation time, the ASG will have the [AZRebalance process](https://docs.aws.amazon.com/autoscaling/ec2/userguide/auto-scaling-benefits.html#AutoScalingBehavior.InstanceUsage) enabled, which means it will actively work to balance the number of instances between AZs, and possibly terminate instances. If your applications could be impacted from sudden termination, you can either suspend the AZRebalance feature, or use a tool for automatic draining upon ASG scale-in such as the [k8s-node-drainer]https://github.com/aws-samples/amazon-k8s-node-drainer. The [AWS Node Termination Handler](https://github.com/aws/aws-node-termination-handler/issues/95) will also support this use-case in the future.
- By default, cluster autoscaler will not terminate nodes running pods in the kube-system namespace. You can override this default behaviour by passing in the `--skip-nodes-with-system-pods=false` flag.
- By default, cluster autoscaler will wait 10 minutes between scale down operations, you can adjust this using the `--scale-down-delay-after-add`, `--scale-down-delay-after-delete`, and `--scale-down-delay-after-failure` flag. E.g. `--scale-down-delay-after-add=5m` to decrease the scale down delay to 5 minutes after a node has been added.
- If you're running multiple ASGs, the `--expander` flag supports three options: `random`, `most-pods` and `least-waste`. `random` will expand a random ASG on scale up. `most-pods` will scale up the ASG that will schedule the most amount of pods. `least-waste` will expand the ASG that will waste the least amount of CPU/MEM resources. In the event of a tie, cluster autoscaler will fall back to `random`.
- If you're managing your own kubelets, they need to be started with the `--provider-id` flag. The provider id has the format `aws:///<availability-zone>/<instance-id>`, e.g. `aws:///us-east-1a/i-01234abcdef`.
- If you want to use regional STS endpoints (e.g. when using VPC endpoint for STS) the env `AWS_STS_REGIONAL_ENDPOINTS=regional` should be set.
* The `/etc/ssl/certs/ca-bundle.crt` should exist by default on ec2 instance in
your EKS cluster. If you use other cluster privision tools like
[kops](https://github.com/kubernetes/kops) with different operating systems
other than Amazon Linux 2, please use `/etc/ssl/certs/ca-certificates.crt` or
correct path on your host instead for the volume hostPath in your cluster
autoscaler manifest.
* If you’re using Persistent Volumes, your deployment needs to run in the same
AZ as where the EBS volume is, otherwise the pod scheduling could fail if it
is scheduled in a different AZ and cannot find the EBS volume. To overcome
this, either use a single AZ ASG for this use case, or an ASG-per-AZ while
enabling
[--balance-similar-node-groups](../../FAQ.md#im-running-cluster-with-nodes-in-multiple-zones-for-ha-purposes-is-that-supported-by-cluster-autoscaler).
Alternately, and depending on your use-case, you might be able to switch from
using EBS to using shared storage that is available across AZs (for each pod
in its respective AZ). Consider AWS services like Amazon EFS or Amazon FSx for
Lustre.
* On creation time, the ASG will have the [AZRebalance
process](https://docs.aws.amazon.com/autoscaling/ec2/userguide/auto-scaling-benefits.html#AutoScalingBehavior.InstanceUsage)
enabled, which means it will actively work to balance the number of instances
between AZs, and possibly terminate instances. If your applications could be
impacted from sudden termination, you can either suspend the AZRebalance
feature, or use a tool for automatic draining upon ASG scale-in such as the
[k8s-node-drainer]https://github.com/aws-samples/amazon-k8s-node-drainer. The
[AWS Node Termination
Handler](https://github.com/aws/aws-node-termination-handler/issues/95) will
also support this use-case in the future.
* By default, cluster autoscaler will not terminate nodes running pods in the
kube-system namespace. You can override this default behaviour by passing in
the `--skip-nodes-with-system-pods=false` flag.
* By default, cluster autoscaler will wait 10 minutes between scale down
operations, you can adjust this using the `--scale-down-delay-after-add`,
`--scale-down-delay-after-delete`, and `--scale-down-delay-after-failure`
flag. E.g. `--scale-down-delay-after-add=5m` to decrease the scale down delay
to 5 minutes after a node has been added.
* If you're running multiple ASGs, the `--expander` flag supports three options:
`random`, `most-pods` and `least-waste`. `random` will expand a random ASG on
scale up. `most-pods` will scale up the ASG that will schedule the most amount
of pods. `least-waste` will expand the ASG that will waste the least amount of
CPU/MEM resources. In the event of a tie, cluster autoscaler will fall back to
`random`.
* If you're managing your own kubelets, they need to be started with the
`--provider-id` flag. The provider id has the format
`aws:///<availability-zone>/<instance-id>`, e.g.
`aws:///us-east-1a/i-01234abcdef`.
* If you want to use regional STS endpoints (e.g. when using VPC endpoint for
STS) the env `AWS_STS_REGIONAL_ENDPOINTS=regional` should be set.