Kubernetes Horizonntal Pod Autoscaling

With Horizontal Pod Autoscaling, Kubernetes 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 Kubernetes 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

Prerequisites

  • 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

Kubernetes Horizontal Pod Autoscaler along with kubectl 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

kubectl top pod

kubectl top node

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

[output]

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  https://github.com/kubernetes-incubator/metrics-server.git
kubectl apply -f metrics-server/deploy/kubernetes/

Validate

kubectl get deploy,pods -n kube-system --selector='k8s-app=metrics-server'

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 https://gist.githubusercontent.com/initcron/1a2bd25353e1faa22a0ad41ad1c01b62/raw/008e23f9fbf4d7e2cf79df1dd008de2f1db62a10/k8s-metrics-server.patch.yaml

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

Now validate with

kubectl top node
kubectl top pod

where expected output shoudl be similar to,

kubectl 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
metadata:
  name: vote
spec:
  minReplicas: 4
  maxReplicas: 45
  targetCPUUtilizationPercentage: 70
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: vote

apply

kubectl apply -f vote-hpa.yaml

Validate

kubectl get hpa

kubectl describe hpa vote

kubectl 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
metadata:
  name: loadtest
spec:
  template:
    spec:
      containers:
      - name: siege
        image: schoolofdevops/loadtest:v1
        command: ["siege",  "--concurrent=5", "--benchmark", "--time=6m", "http://vote"]
      restartPolicy: Never
  backoffLimit: 4

And launch the loadtest

kubectl apply -f loadtest-job.yaml

To monitor while the load test is running ,

watch kubectl top pods

To get information about the job

kubectl get jobs
kubectl describe  job loadtest

To check the load test output

kubectl 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:~# kubectl 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,

kubectl get jobs
NAME            DESIRED   SUCCESSFUL   AGE
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

Summary

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

Reading List