Auto Scaling with Openshift

With Horizontal Pod Autoscaling, openshift automatically scales the number of pods in a replication controller, deployment or replica set based on observed CPU utilization (or, with alpha support, on some other, application-provided metrics).

The Horizontal Pod Autoscaler is implemented as a openshift API resource and a controller. The resource determines the behavior of the controller. The controller periodically adjusts the number of replicas in a replication controller or deployment to match the observed average CPU utilization to the target specified by user


  • Metrics Server. This needs to be setup if you are using kubeadm etc. and replaces heapster starting with kubernetes version 1.8.
  • Resource Requests and Limits. Defining CPUas well as Memory requirements for containers in Pod Spec is a must

Deploying Metrics Server

openshift Horizontal Pod Autoscaler along with oc adm top command depends on the core monitoring data such as cpu and memory utilization which is scraped and provided by kubelet, which comes with in built cadvisor component. Earlier, you would have to install a additional component called heapster in order to collect this data and feed it to the hpa controller. With 1.8 version of kubernetes, this behavior is changed, and now metrics-server would provide this data. Metric server is being included as a essential component for kubernetes cluster, and being incroporated into kubernetes to be included out of box. It stores the core monitoring information using in-memory data store.

If you try to pull monitoring information using the following commands

oc adm top pod

oc adm top node

it does not show it, rather gives you a error message similar to


Error from server (NotFound): the server could not find the requested resource (get services http:heapster:)

Even though the error mentions heapster, its replaced with metrics server by default now.

Deploy metric server with the following commands,

cd ~
git clone
oc apply -f metrics-server/deploy/1.8+/


oc get deploy,pods -n kube-system

Monitoring has been setup.

Fixing issues with Metrics deployment

There is a known issue as off Dec 2018 with Metrics Server where is fails to work event after deploying it using above commands. This can be fixed with a patch using steps below.

To apply a patch to metrics server,

wget -c

oc patch deploy metrics-server -p "$(cat k8s-metrics-server.patch.yaml)" -n kube-system

Now validate with

oc adm top node
oc adm top pod

where expected output shoudl be similar to,

oc top node

NAME     CPU(cores)   CPU%   MEMORY(bytes)   MEMORY%
vis-01   145m         7%     2215Mi          57%
vis-13   36m          1%     1001Mi          26%
vis-14   71m          3%     1047Mi          27%

Create a HPA

To demonstrate Horizontal Pod Autoscaler we will use a custom docker image based on the php-apache image

file: vote-hpa.yaml

apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
  name: vote
  minReplicas: 4
  maxReplicas: 15
  targetCPUUtilizationPercentage: 40
    kind: DeploymentConfig
    name: vote


oc apply -f vote-hpa.yaml


oc get hpa

oc describe hpa vote

oc get pod,deploy

If you have a monitoring system such as grafana, you could also view the graphs for vote deployment.

Monitoring Deployments with grafana

Load Test

file: loadtest-job.yaml

apiVersion: batch/v1
kind: Job
  name: loadtest
      - name: siege
        image: schoolofdevops/loadtest:v1
        command: ["siege",  "--concurrent=5", "--benchmark", "--time=10m", "http://vote"]
      restartPolicy: Never
  backoffLimit: 4

And launch the loadtest

oc apply -f loadtest-job.yaml

To monitor while the load test is running ,

watch oc top pods

To get information about the job

oc get jobs
oc describe  job loadtest

To check the load test output

oc logs  -f loadtest-xxxx

[replace loadtest-xxxx with the actual pod id.]

[Sample Output]

** SIEGE 3.0.8
** Preparing 15 concurrent users for battle.
root@kube-01:~# oc logs vote-loadtest-tv6r2 -f
** SIEGE 3.0.8
** Preparing 15 concurrent users for battle.


Lifting the server siege...      done.

Transactions:              41618 hits
Availability:              99.98 %
Elapsed time:             299.13 secs
Data transferred:         127.05 MB
Response time:              0.11 secs
Transaction rate:         139.13 trans/sec
Throughput:             0.42 MB/sec
Concurrency:               14.98
Successful transactions:       41618
Failed transactions:               8
Longest transaction:            3.70
Shortest transaction:           0.00

FILE: /var/log/siege.log
You can disable this annoying message by editing
the .siegerc file in your home directory; change
the directive 'show-logfile' to false.

Now check the job status again,

oc get jobs
vote-loadtest   1         1            10m

  • Keep monitoring for the load on the pod as the job progresses.
  • Keep a watch from grafana as well to see the resource utilisation for vote deployment.
  • You should see hpa in action as it scales out/in the vote deployment with the increasing/decreasing load.

Monitoring Deployments with grafana


In this lab, you have successfull configured and demonstrated dynamic scaling ability of openshift using horizontalpodautoscalers. You have also learnt about a new jobs controller type for running one off or batch jobs.

Reading List