For both autoscaling guides, we'll need a cluster.
For Cluster Autoscaler You need a cloud based cluster that supports the cluster autoscaler
For HPA We'll use kind

Cluster Autoscaling - Creating an AKS Cluster

# azure example

NAME=aks-getting-started
RESOURCEGROUP=aks-getting-started
SERVICE_PRINCIPAL=
SERVICE_PRINCIPAL_SECRET=

az aks create -n $NAME \
--resource-group $RESOURCEGROUP \
--location australiaeast \
--kubernetes-version 1.16.10 \
--nodepool-name default \
--node-count 1 \
--node-vm-size Standard_F4s_v2  \
--node-osdisk-size 250 \
--service-principal $SERVICE_PRINCIPAL \
--client-secret $SERVICE_PRINCIPAL_SECRET \
--output none \
--enable-cluster-autoscaler \
--min-count 1 \
--max-count 5

Horizontal Pod Autocaling - Creating a Kind Cluster

My Node has 6 CPU cores for this demo

kind create cluster --name hpa --image kindest/node:v1.18.4

Metric Server

For Cluster Autoscaler - On cloud-based clusters, Metric server may already be installed.
For HPA - We're using kind

Metric Server provides container resource metrics for use in autoscaling pipelines

Because I run K8s 1.18 in kind, the Metric Server version i need is 0.3.7
We will need to deploy Metric Server 0.3.7
I used components.yamlfrom the release page link above.

Important Note : For Demo clusters (like kind), you will need to disable TLS
You can disable TLS by adding the following to the metrics-server container args

For production, make sure you remove the following :

- --kubelet-insecure-tls
- --kubelet-preferred-address-types="InternalIP"

Deployment:

cd kubernetes\autoscaling
kubectl -n kube-system apply -f .\metric-server\metricserver-0.3.7.yaml

#test 
kubectl -n kube-system get pods

#wait for metrics to populate
kubectl top nodes

Example Application

For all autoscaling guides, we'll need a simple app, that generates some CPU load

Build the app
Push it to a registry
Ensure resource requirements are set
Deploy it to Kubernetes
Ensure metrics are visible for the app

# build

cd kubernetes\autoscaling\application-cpu
docker build . -t aimvector/application-cpu:v1.0.0

# push
docker push aimvector/application-cpu:v1.0.0

# resource requirements
resources:
  requests:
    memory: "50Mi"
    cpu: "500m"
  limits:
    memory: "500Mi"
    cpu: "2000m"

# deploy 
kubectl apply -f deployment.yaml

# metrics
kubectl top pods

Generate some traffic

# get a terminal to the traffic-generator
kubectl exec -it traffic-generator sh
# install wrk
apk add --no-cache wrk curl

# simulate some load
wrk -c 5 -t 5 -d 99999 -H "Connection: Close" http://application-cpu

kubectl scale deploy/application-cpu --replicas 2