This project is a fork of OpenAI's Kubernetes-ec2-autoscaler

kubernetes-acs-autoscaler is a node-level autoscaler for Kubernetes on Azure Container Services. Kubernetes is a container orchestration framework that schedules Docker containers on a cluster, and kubernetes-acs-autoscaler can scale based on the pending job queue.

The autoscaler can be run anywhere as long as it can access the Azure and Kubernetes APIs, but the recommended way is to set it up as a Kubernetes Pod.

You need to provide a Service Principal to the autoscaler. You can create one using Azure CLI:
az ad sp create-for-rbac

The best way to use the access key in Kubernetes is with a secret.

Here is a sample format for secret.yaml:

apiVersion: v1
kind: Secret
metadata:
  name: autoscaler
  namespace: system
data:
  azure-sp-app-id: [base64 encoded access key]
  azure-sp-secret: [base64 encoded secret access key]
  azure-sp-tenant-id: [optional slack incoming webhook]

You can then save it in Kubernetes:

$ kubectl create -f secret.yaml

scaling-controller.yaml has an example Replication Controller that will set up Kubernetes to always run exactly one copy of the autoscaler. To create the Replication Controller:

$ kubectl create -f scaling-controller.yaml

You should then be able to inspect the pod's status and logs:

$ kubectl get pods --namespace system -l app=autoscaler
NAME               READY     STATUS    RESTARTS   AGE
autoscaler-opnax   1/1       Running   0          3s

$ kubectl logs autoscaler-opnax --namespace system
2016-08-25 20:36:45,985 - autoscaler.cluster - DEBUG - Using kube service account
2016-08-25 20:36:45,987 - autoscaler.cluster - INFO - ++++++++++++++ Running Scaling Loop ++++++++++++++++
2016-08-25 20:37:04,221 - autoscaler.cluster - INFO - ++++++++++++++ Scaling Up Begins ++++++++++++++++
...

$ python main.py [options]

• --container-service-name: Name of ACS cluster.
• --resource-group: Name of the resource group containing the cluster
• --template-file: (For acs-engine only) Full path to the ARM template file.
• --parameters-file: (For acs-engine only) Full path to the ARM parameters file.
• --kubeconfig: Path to kubeconfig YAML file. Leave blank if running in Kubernetes to use service account.
• --idle-threshold: This defines the maximum duration (in seconds) for an instance to be kept idle.
• --service-principal-app-id: Azure service principal id. Can also be specified in environment variable AZURE_SP_APP_ID
• --service-principal-secret: Azure service principal secret. Can also be specified in environment variable AZURE_SP_SECRET
• --service-principal-tenant-id: Azure service princiap tenant id. Can also be specified in environment variable AZURE_SP_TENANT_ID
• --instance-init-time: Maximum duration (in seconds) after an instance is launched before being considered unhealthy (running in EC2 but not joining the Kubernetes cluster)
• --sleep: Time (in seconds) to sleep between scaling loops (to be careful not to run into AWS API limits)
• --slack-hook: Optional Slack incoming webhook for scaling notifications
• --dry-run: Flag for testing so resources aren't actually modified. Actions will instead be logged only.
• -v: Sets the verbosity. Specify multiple times for more log output, e.g. -vvv
• --debug: Do not catch errors. Explicitly crash.

Pass the name of your container service to --container-service-name.
--template-file and --parameters-file should not be provided (reserved for acs-engine)

Pass the template (azuredeploy.json) and parameter (azuredeploy.parameters.json) files that you generated with acs-engine to the autoscaler through --template-file and --parameters-file.
--container-service-name should be left empty as it is reserved for ACS.

$ docker build -t autoscaler .
$ ./devenvh.sh
$ python main.py --resource-group k8s --container-service-name containerservice-k8s --service-principal-app-id 'XXXXXXXXX' --service-principal-secret 'XXXXXXXXXXXXX' service-principal-tenant-id 'XXXXXX' --dry-run -vvv --kubeconfig /root/.kube/config