Cluster Turndown

Cluster Turndown is an automated scaledown and scaleup of a Kubernetes cluster's backing nodes based on a custom schedule and turndown criteria. This feature can be used to reduce spend during down hours and/or reduce surface area for security reasons. The most common use case is to scale non-prod environments (e.g. dev clusters) to zero during off hours. The project currently supports clusters on GKE, EKS, and kops on AWS.

⚠️ If you are upgrading from a pre-2.0.0 version of cluster-turndown, you will have to migrate your custom resources. turndownschedules.kubecost.k8s.io has been changed to turndownschedules.kubecost.com and finalizers.kubecost.k8s.io has been changed to finalizers.kubecost.com. See #44 for an explanation. ⚠️

Note: Cluster Turndown is currently in ALPHA

GKE Setup

We have provided a shell script capable of performing the required steps in setting up a service account for use with cluster-turndown. More info

Running the Setup Script

To use this setup script supply the following parameters:

$ ./scripts/gke-create-service-key.sh <Project ID> <Service Account Name>

The parameters to supply the script are as follows:

• Project ID: The GCP project identifier you can find via: gcloud config get-value project
• Service Account Name: The desired service account name to create, e.g. turndown-service

---

EKS & AWS kops Setup

For EKS cluster provisioning, if using eksctl, make sure that you use the --managed option when creating the cluster. Unmanaged nodegroups should be upgraded to managed. Learn more in eksctl's EKS managed nodegroups doc.

Create a new User or IAM role with AutoScalingFullAccess permissions.

JSON definition of those permissions:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "autoscaling:*",
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": "cloudwatch:PutMetricAlarm",
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "ec2:DescribeAccountAttributes",
                "ec2:DescribeAvailabilityZones",
                "ec2:DescribeImages",
                "ec2:DescribeInstanceAttribute",
                "ec2:DescribeInstances",
                "ec2:DescribeKeyPairs",
                "ec2:DescribeLaunchTemplateVersions",
                "ec2:DescribePlacementGroups",
                "ec2:DescribeSecurityGroups",
                "ec2:DescribeSpotInstanceRequests",
                "ec2:DescribeSubnets",
                "ec2:DescribeVpcClassicLink"
            ],
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "elasticloadbalancing:DescribeLoadBalancers",
                "elasticloadbalancing:DescribeTargetGroups"
            ],
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": "iam:CreateServiceLinkedRole",
            "Resource": "*",
            "Condition": {
                "StringEquals": {
                    "iam:AWSServiceName": "autoscaling.amazonaws.com"
                }
            }
        }
    ]
}

For EKS clusters, add the following permissions to the above policy for EKS API access:

{
    "Effect": "Allow",
    "Action": [
        "eks:ListClusters",
        "eks:DescribeCluster",
        "eks:DescribeNodegroup",
        "eks:ListNodegroups",
        "eks:CreateNodegroup",
        "eks:UpdateClusterConfig",
        "eks:UpdateNodegroupConfig",
        "eks:DeleteNodegroup",
        "eks:ListTagsForResource",
        "eks:TagResource",
        "eks:UntagResource"
    ],
    "Resource": "*"
}

Create a new file, service-key.json, and use the access key id and secret access key to fill out the following template:

{
    "aws_access_key_id": "<ACCESS_KEY_ID>",
    "aws_secret_access_key": "<SECRET_ACCESS_KEY>"
}

Then run the following to create the turndown namespace:

$ kubectl apply -f artifacts/turndown-namespace.yaml

Then run the following to create the secret:

$ kubectl create secret generic cluster-turndown-service-key -n turndown --from-file=service-key.json

---

Deploying

After completing setup, run the following command to get the cluster-turndown pod running on your cluster:

$ kubectl apply -f https://github.com/kubecost/cluster-turndown/releases/latest/download/cluster-turndown-full.yaml

In this yaml, you'll find the definitions for the following:

• ServiceAccount
• ClusterRole
• ClusterRoleBinding
• Deployment

Verify the Pod is Running

You can verify that the pod is running by issuing the following:

$ kubectl get pods -l app=cluster-turndown -n turndown

---

Setting a Turndown Schedule

Cluster Turndown uses a Kubernetes Custom Resource Definition to create schedules. There is an example resource located at artifacts/example-schedule.yaml:

apiVersion: kubecost.com/v1alpha1
kind: TurndownSchedule
metadata:
  name: example-schedule
  finalizers:
  - "finalizer.kubecost.com"
spec:
  start: 2020-03-12T00:00:00Z
  end: 2020-03-12T12:00:00Z
  repeat: daily

This definition will create a schedule that starts by turning down at the designated start date-time and turning back up at the designated end date-time. Both the start and end times should be in RFC3339 format, i.e. times based on offsets to UTC. There are three possible values for repeat:

• none: Single schedule turndown and turnup.
• daily: Start and End times will reschedule every 24 hours.
• weekly: Start and End times will reschedule every 7 days.

To create this schedule, you may modify example-schedule.yaml to your desired schedule and run:

$ kubectl apply -f artifacts/example-schedule.yaml

Currently, updating a resource is not supported, so if the scheduling of the example-schedule fails, you will need to delete the resource via:

$ kubectl delete tds example-schedule

Then make the modifications to the schedule and re-apply.

Viewing a Turndown Schedule

The turndownschedule resource can be listed via kubectl as well:

$ kubectl get turndownschedules

or using the shorthand:

$ kubectl get tds

Details regarding the status of the turndown schedule can be found by outputting as json or yaml:

$ kubectl get tds example-schedule -o yaml

apiVersion: kubecost.com/v1alpha1
kind: TurndownSchedule
metadata:
  annotations:
    kubectl.kubernetes.io/last-applied-configuration: |
      {"apiVersion":"kubecost.com/v1alpha1","kind":"TurndownSchedule","metadata":{"annotations":{},"finalizers":["finalizer.kubecost.com"],"name":"example-schedule"},"spec":{"end":"2020-03-17T00:35:00Z","repeat":"daily","start":"2020-03-17T00:20:00Z"}}
  creationTimestamp: "2020-03-17T00:18:39Z"
  finalizers:
  - finalizer.kubecost.com
  generation: 1
  name: example-schedule
  resourceVersion: "33573"
  selfLink: /apis/kubecost.com/v1alpha1/turndownschedules/example-schedule
  uid: d9b16aed-67e4-11ea-b591-42010a8e0075
spec:
  end: "2020-03-17T00:35:00Z"
  repeat: daily
  start: "2020-03-17T00:20:00Z"
status:
  current: scaledown
  lastUpdated: "2020-03-17T00:36:39Z"
  nextScaleDownTime: "2020-03-18T00:21:38Z"
  nextScaleUpTime: "2020-03-18T00:36:38Z"
  scaleDownId: 38ebf595-4e2b-46e9-951a-1e3ceff30536
  scaleDownMetadata:
    repeat: daily
    type: scaledown
  scaleUpID: 869ec89f-a8d8-450b-9ebb-71cd4d7fbaf8
  scaleUpMetadata:
    repeat: daily
    type: scaleup
  state: ScheduleSuccess

The Status field displays the current status of the schedule including next schedule times, specific schedule identifiers, and the overall state of schedule.

• State: The state of the turndown schedule. This can be:
  • ScheduleSuccess: The schedule has been set and is waiting to run.
  • ScheduleFailed: The scheduling failed due to a schedule already existing, scheduling for a date-time in the past.
  • ScheduleCompleted: For schedules with repeat: none, the schedule will move to a completed state after turn up.
• Current: The next action to run.
• LastUpdated: The last time the status was updated on the schedule.
• NextScaleDownTime: The next time a turndown will be executed.
• NextScaleUpTime: The next time at turn up will be executed.
• ScaleDownId: Specific identifier assigned by the internal scheduler for turndown.
• ScaleUpId: Specific identifier assigned by the internal scheduler for turn up.
• ScaleDownMetadata: Metadata attached to the scaledown job, assigned by the turndown scheduler.
• ScaleUpMetadata: Metadata attached to the scale up job, assigned by the turndown scheduler.

Cancelling a Turndown Schedule

A turndown can be cancelled before turndown actually happens or after. This is performed by deleting the resource:

$ kubectl delete tds example-schedule

Note that cancelling while turndown is in the act of scaling down or scaling up will result in a delayed cancellation, as the schedule must complete it's operation before processing the deletion/cancellation.

If the turndown schedule is cancelled between a turndown and turn up, the turn up will occur automatically upon cancel.

Limitations

• The internal scheduler only allows one schedule at a time to be used. Any additional schedule resources created will fail (kubectl get tds -o yaml will display the status).
• DO NOT attempt to kubectl edit a turndown schedule. This is currently not supported. Recommended approach for modifying is to delete and then create a new schedule.
• 20-minute minimum time window between start and end of turndown schedule

How it works

Managed Cluster Strategy (e.g. GKE + EKS)

When the turndown schedule occurs, a new node pool with a single g1-small node is created. Taints are added to this node to only allow specific pods to be scheduled there. We update our cluster-turndown deployment such that the turndown pod is allowed to schedule on the singleton node. Once the pod is moved to the new node, it will start back up and resume scaledown. This is done by cordoning all nodes in the cluster (other than our new g1-small node), and then reducing the node pool sizes to 0.

GKE Autoscaler Strategy

Whenever there exists at least one NodePool with the cluster-autoscaler enabled, the turndown will

1. Resize all non-autoscaling nodepools to 0

2. Schedule the turndown pod on one of the autoscaler nodepool nodes

3. Once it is brought back up (rescheduled to the selected node), the turndown pod will start a process called "flattening" which attempts to set deployment replicas to 0, turn off jobs, and annotate pods with labels that allow the autoscaler to do the rest of the work. Flattening persists pre-turndown values in the annotations of Kubernetes objects.

   The GKE autoscaler behavior is expected to handle the rest: removing now-unneeded nodes from the node pools. A limitation of this strategy is that the autoscaled node pools won't go below their configured minimum node count.

4. When turn up occurs, deployments and daemonsets are "expanded" to their original sizes/replicas.

There are four annotations that can be applied for this process:

• kubecost.kubernetes.io/job-suspend: Stores a bool containing the previous paused state of a kubernetes CronJob.
• kubecost.kubernetes.io/turn-down-replicas: Stores the previous number of replicas set on the deployment.
• kubecost.kubernetes.io/turn-down-rollout: Stores the previous maxUnavailable for the deployment rollout.
• kubecost.kubernetes.io/safe-evict: For autoscaling clusters, we use the cluster-autoscaler.kubernetes.io/safe-to-evict to have the autoscaler do the work for us. We want to make sure we preserve any deployments that previously had this annotation set, so when we scale back up, we don’t reset this value unintentionally.

AWS kops Strategy

This turndown strategy schedules the turndown pod on the Master node, then resizes all Auto Scaling Groups other than the master to 0. Similar to flattening in GKE, the previous min/max/current values of the ASG prior to turndown will be set on the tag. When turn up occurs, those values can be read from the tags and restored to their original sizes. For the standard strategy, turn up will reschedule the turndown pod off the Master upon completion (occurs 5 minutes after turn up). This is to allow any modifications via kops without resetting any cluster specific scheduling setup by turndown. The tag label used to store the min/max/current values for a node group is cluster.turndown.previous. Once turn up happens and the node groups are resized to their original size, the tag is deleted.