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137 lines
3.3 KiB
Markdown
137 lines
3.3 KiB
Markdown
# Cluster Autoscaling
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Scales the number of nodes in our cluster based off usage metrics
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[Documentation](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler)
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## Understanding Resources
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In this example, I'll be focusing on CPU for scaling. <br/>
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We need to ensure we have an understanding of the compute resources we have. <br/>
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1) How many cores do we have <br/>
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2) How many cores do our application use <br/>
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I go into more details about pod resource utilisation in the Horizontal Pod Autoscaler guide.
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# We need a Kubernetes cluster with Cluster Autoscaler
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```
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# azure example
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NAME=aks-getting-started
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RESOURCEGROUP=aks-getting-started
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SERVICE_PRINCIPAL=
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SERVICE_PRINCIPAL_SECRET=
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az aks create -n $NAME \
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--resource-group $RESOURCEGROUP \
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--location australiaeast \
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--kubernetes-version 1.16.10 \
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--nodepool-name default \
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--node-count 1 \
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--node-vm-size Standard_F4s_v2 \
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--node-osdisk-size 250 \
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--service-principal $SERVICE_PRINCIPAL \
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--client-secret $SERVICE_PRINCIPAL_SECRET \
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--output none \
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--enable-cluster-autoscaler \
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--min-count 1 \
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--max-count 5
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```
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# Deploy Metric Server
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[Metric Server](https://github.com/kubernetes-sigs/metrics-server) provides container resource metrics for use in autoscaling pipelines
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We will need to deploy Metric Server [0.3.7](https://github.com/kubernetes-sigs/metrics-server/releases/tag/v0.3.7) <br/>
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I used `components.yaml`from the release page link above. <br/>
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Note: For Demo clusters (like `kind`), you will need to disable TLS <br/>
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You can disable TLS by adding the following to the metrics-server container args
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```
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- --kubelet-insecure-tls
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- --kubelet-preferred-address-types="InternalIP"
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```
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Deploy it:
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```
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cd kubernetes\autoscaling
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kubectl -n kube-system apply -f .\metric-server\metricserver-0.3.7.yaml
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#test
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kubectl -n kube-system get pods
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#wait for metrics to populate
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kubectl top nodes
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```
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## Example App
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We have an app that simulates CPU usage
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```
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# build
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cd kubernetes\autoscaling\application-cpu
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docker build . -t aimvector/application-cpu:v1.0.0
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# push
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docker push aimvector/application-cpu:v1.0.0
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# resource requirements
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resources:
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requests:
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memory: "50Mi"
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cpu: "500m"
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limits:
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memory: "500Mi"
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cpu: "2000m"
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# deploy
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kubectl apply -f deployment.yaml
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# metrics
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kubectl top pods
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```
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## Generate some CPU load
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```
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# Deploy a tester to run traffic from
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cd kubernetes/autoscaling
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kubectl apply -f ./autoscaler-cluster/tester.yaml
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# get a terminal
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kubectl exec -it tester sh
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# install wrk
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apk add --no-cache wrk curl
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# simulate some load
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wrk -c 5 -t 5 -d 99999 -H "Connection: Close" http://application-cpu
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# scale and keep checking `kubectl top`
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# every time we add a pod, CPU load per pod should drop dramatically.
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# roughly 8 pods will have each pod use +- 400m
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kubectl scale deploy/application-cpu --replicas 2
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```
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## Deploy an autoscaler
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```
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# scale the deployment back down to 2
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kubectl scale deploy/application-cpu --replicas 2
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# deploy the autoscaler
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kubectl autoscale deploy/application-cpu --cpu-percent=95 --min=1 --max=10
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# pods should scale to roughly 7-8 to match criteria
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kubectl describe hpa/application-cpu
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kubectl get hpa/application-cpu -owide
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```
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