Scaling based on CPU utilization

Console

1. In the console, go to the Instance groups page.

   Go to Instance groups

2. If you have an instance group, select it and click Edit. If you don't have an instance group, click Create instance group.

3. If no autoscaling configuration exists, under Autoscaling, click Configure autoscaling.

4. Under Autoscaling mode, select On: add and remove instances to the group to enable autoscaling.

5. Specify the minimum and maximum numbers of instances that you want the autoscaler to create in this group.

6. In the Autoscaling signals section, if an existing CPU utilization metric doesn't yet exist, add one:

   1. Click Add a signal.
   2. In the Signal type drop-down, select CPU utilization.
   3. Enter the Target CPU utilization that you want. This value is treated as a percentage. For example, for 75% CPU utilization, enter 75.
   4. Under Predictive autoscaling, select Off. To learn more about predictive autoscaling, and whether it is suitable for your workload, see Scaling based on predictions.
   5. Click Done.

7. You can use the Initialization period to tell the autoscaler how long it takes for your application to initialize. Specifying an accurate initialization period improves autoscaler decisions. For example, when scaling out, the autoscaler ignores data from VMs that are still initializing because those VMs might not yet represent normal usage of your application. The default initialization period is 60 seconds.

8. Click Save.

gcloud

Use the set-autoscaling sub-command to enable autoscaling for a managed instance group. For example, the following command creates an autoscaler that has a target CPU utilization of 60%. Along with the --target-cpu-utilization parameter, the --max-num-replicas parameter is also required when creating an autoscaler:

gcloud compute instance-groups managed set-autoscaling example-managed-instance-group \
    --max-num-replicas 20 \
    --target-cpu-utilization 0.60 \
    --cool-down-period 90

You can use the --cool-down-period flag to set the initialization period, which tells the autoscaler how long it takes for your application to initialize. Specifying an accurate initialization period improves autoscaler decisions. For example, when scaling out, the autoscaler ignores data from VMs that are still initializing because those VMs might not yet represent normal usage of your application. The default initialization period is 60 seconds.

Optionally, you can enable predictive autoscaling to scale out ahead of predicted load. To learn whether predictive autoscaling is suitable for your workload, see Scaling based on predictions.

You can verify that autoscaling is successfully enabled by using the instance-groups managed describe sub-command, which describes the corresponding managed instance group and provides information about any autoscaling features for that instance group:

gcloud compute instance-groups managed describe example-managed-instance-group

For a list of available gcloud commands and flags, see the gcloud reference.

REST

To create an autoscaler, use the autoscalers.insert method for a zonal MIG or the regionAutoscalers.insert method for a regional MIG.

The following example creates an autoscaler for a zonal MIG:

POST https://compute.googleapis.com/compute/v1/projects/PROJECT_ID/zones/ZONE/autoscalers/

Your request body must contain the name, target, and autoscalingPolicy fields. autoscalingPolicy must define cpuUtilization and maxNumReplicas.

You can use the coolDownPeriodSec field to set the initialization period, which tells the autoscaler how long it takes for your application to initialize. Specifying an accurate initialization period improves autoscaler decisions. For example, when scaling out, the autoscaler ignores data from VMs that are still initializing because those VMs might not yet represent normal usage of your application. The default initialization period is 60 seconds.

Optionally, you can enable predictive autoscaling to scale out ahead of predicted load. To learn whether predictive autoscaling is suitable for your workload, see Scaling based on predictions.

{
  "name": "example-autoscaler",
  "target": "https://www.googleapis.com/compute/v1/projects/myproject/zones/us-central1-f/instanceGroupManagers/example-managed-instance-group",
  "autoscalingPolicy": {
    "maxNumReplicas": 10,
    "cpuUtilization": {
      "utilizationTarget": 0.6
    },
    "coolDownPeriodSec": 90
  }
}