Dit is een stapsgewijze handleiding voor het instellen van Kubernetes op Scaleway bare-metal ARM en x86-64. De belangrijkste reden dat ik aan dit project heb gewerkt, is dat ik het maken van testomgevingen voor OpenFaaS en Weave Net op ARM wilde automatiseren. Ik was op zoek naar een goedkope oplossing om integratietesten uit te voeren en na het uitproberen van verschillende cloudproviders ben ik op Scaleway uitgekomen. Scaleway is een Franse cloudprovider die bare-metal ARM- en x86-64-servers aanbiedt tegen betaalbare prijzen. Met behulp van de Terraform Scaleway-provider in combinatie met kubeadm kunt u binnen tien minuten een volledig functioneel Kubernetes-cluster hebben

 Initiële setup
Kloon de repository en installeer de afhankelijkheden:
$ git clone httpsgithub.com/stefanprodan/k8s-scw-baremetal.git $ cd k8s-scw-baremetal $ terraform init
Merk op dat je Terraform v0.10 of nieuwer nodig hebt om dit project uit te voeren

Voordat u het project uitvoert, moet u een toegangstoken voor Terraform maken om verbinding te maken met de Scaleway API. Maak met behulp van het token en uw toegangssleutel twee omgevingsvariabelen:
$ export SCALEWAY_ORGANIZATIONACCESS-KEY>"$ export SCALEWAY_TOKENACCESS-TOKEN>"Gebruik
Maak een ARMv7 bare-metal Kubernetes-cluster met één master en twee nodes:
$ terraform werkruimte nieuwe arm $ terraform toepassen \ -var region=par1 \ -var arch=arm \ -var server_type=C1 \ -var nodes=2 \ -var weave_passwd=ChangeMe \ -var k8s_version=stable-1.9 \ -var docker_version =17.03.0~ce-0~ubuntu-xenial
Dit zal het volgende doen:
- reserveert openbare IP's voor elke server
- voorziet drie bare-metal servers met Ubuntu 16.04.1 LTS
- maakt verbinding met de masterserver via SSH en installeert Docker CE- en kubeadm armhf apt-pakketten
- voert kubeadm init uit op de hoofdserver en configureert kubectl
- downloadt het configuratiebestand van de kubectl-beheerder op uw lokale computer en vervangt het privé-IP-adres door het openbare IP-adres
- creëert een Kubernetes-geheim met het Weave Net-wachtwoord
- installeert Weave Net met gecodeerde overlay
- installeert cluster-add-ons (Kubernetes-dashboard, metrische server en Heapster)
- start de werkknooppunten parallel en installeert Docker CE en kubeadm
- voegt zich bij de werkknooppunten in het cluster met behulp van het kubeadm-token dat is verkregen van de master
Opschalen door het aantal nodes te vergroten:
$ terraform toepassen -var nodes=3
Breek de hele infrastructuur af met:
terraform-kracht
Maak een AMD64 bare-metal Kubernetes-cluster met één master en een node:
$ terraform-werkruimte nieuw amd64 $ terraform van toepassing \ -var region=par1 \ -var arch=x86_64 \ -var server_type=C2S \ -var nodes=1 \ -var weave_passwd=ChangeMe \ -var k8s_version=stable-1.9 \ -var docker_version =17.03.0~ce-0~ubuntu-xenial
 Afstandsbediening
Na het toepassen van het Terraform-plan ziet u verschillende uitvoervariabelen, zoals het openbare IP-adres van de master, de opdracht kubeadmn join en de huidige configuratie van de werkruimtebeheerder

Om te kunnen rennen
kubectl-opdrachten tegen het Scaleway-cluster kunt u de
kubectl_config uitvoervariabele:
Controleer of Heapster werkt:
$ kubectl --kubeconfig terraform output kubectl_config) top nodes NAAM CPU(cores) CPU% GEHEUGEN(bytes) GEHEUGEN% arm-master-1 655m 16% 873Mi 45% arm-node-1 147m 3% 618Mi 32% arm-node- 2 101m 2% 584Mi 30%
De
kubectl configuratiebestandsindeling is
.conf as in
arm.conf or
amd64.conf

In order to access the dashboard you’ll need to find its cluster IP:
$ kubectl --kubeconfig terraform output kubectl_config) \ -n kube-system get svc --selector=k8s-app=kubernetes-dashboard NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE kubernetes-dashboard ClusterIP 10.107.37.220  80/TCP 6m
Open a SSH tunnel:
ssh -L 8888::80 [email protected]
Now you can access the dashboard on your computer at
httplocalhost:8888

 Expose services outside the cluster
Since we’re running on bare-metal and Scaleway doesn’t offer a load balancer, the easiest way to expose applications outside of Kubernetes is using a NodePort service

Let’s deploy the podinfo app in the default namespace. Podinfo has a multi-arch Docker image and it will work on arm, arm64 or amd64

Create the podinfo nodeport service:
$ kubectl --kubeconfig terraform output kubectl_config) \ apply -f httpsraw.githubusercontent.com/stefanprodan/k8s-podinfo/master/deploy/auto-scaling/podinfo-svc-nodeport.yaml service "podinfo-nodeport" created
Create the podinfo deployment:
$ kubectl --kubeconfig terraform output kubectl_config) \ apply -f httpsraw.githubusercontent.com/stefanprodan/k8s-podinfo/master/deploy/auto-scaling/podinfo-dep.yaml deployment "podinfo" created
Inspect the podinfo service to obtain the port number:
$ kubectl --kubeconfig terraform output kubectl_config) \ get svc --selector=app=podinfo NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE podinfo-nodeport NodePort 10.104.132.14  9898:31190/TCP 3m
You can access podinfo at
httpMASTER_PUBLIC_IP>:31190 or using curl:

$ curl httpterraform output k8s_master_public_ip):31190 runtime: arch: arm max_procs: "4" num_cpu: "4" num_goroutine: "12" os: linux version: go1.9.2 labels: app: podinfo pod-template-hash: "1847780700" annotations: kubernetes.io/config.seen: 2018-01-08T00:39:45.580597397Z kubernetes.io/config.source: api environment: HOME: /root HOSTNAME: podinfo-5d8ccd4c44-zrczc KUBERNETES_PORT: tcp10.96.0.1:443 KUBERNETES_PORT_443_TCP: tcp10.96.0.1:443 KUBERNETES_PORT_443_TCP_ADDR: 10.96.0.1 KUBERNETES_PORT_443_TCP_PORT: "443" KUBERNETES_PORT_443_TCP_PROTO: tcp KUBERNETES_SERVICE_HOST: 10.96.0.1 KUBERNETES_SERVICE_PORT: "443" KUBERNETES_SERVICE_PORT_HTTPS: "443" PATH: /usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin externalIP: IPv4: 163.172.139.112
 OpenFaaS
You can deploy OpenFaaS on Kubernetes with Helm or by using the YAML files form the faas-netes repository

Clone the faas-netes repo:
git clone httpsgithub.com/openfaas/faas-netes cd faas-netes
Deploy OpenFaaS for ARM:
$ kubectl --kubeconfig terraform output kubectl_config) \ apply -f ./namespaces.ymlyaml_armhf
Deploy OpenFaaS for AMD64:
$ kubectl --kubeconfig terraform output kubectl_config) \ apply -f ./namespaces.ymlyaml
You can access the OpenFaaS gateway at
httpMASTER_PUBLIC_IP>:31112

 Horizontal Pod Autoscaling
Starting from Kubernetes 1.9
kube-controller-manager is configured by default with
horizontal-pod-autoscaler-use-rest-clients

In order to use HPA we need to install the metrics server to enable the new metrics API used by HPA v2

Both Heapster and the metrics server have been deployed from Terraform
when the master node was provisioned

The metric server collects resource usage data from each node using Kubelet Summary API. Check if the metrics server is running:
$ kubectl --kubeconfig terraform output kubectl_config) \ get --raw "/apis/metrics.k8s.io/v1beta1/nodes" | jq
{ "kind": "NodeMetricsList", "apiVersion": "metrics.k8s.io/v1beta1", "metadata": { "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes" }, "items": [ { "metadata": { "name": "arm-master-1", "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/arm-master-1", "creationTimestamp": "2018-01-08T15:17:09Z" }, "timestamp": "2018-01-08T15:17:00Z", "window": "1m0s", "usage": { "cpu": "384m", "memory": "935792Ki" } }, { "metadata": { "name": "arm-node-1", "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/arm-node-1", "creationTimestamp": "2018-01-08T15:17:09Z" }, "timestamp": "2018-01-08T15:17:00Z", "window": "1m0s", "usage": { "cpu": "130m", "memory": "649020Ki" } }, { "metadata": { "name": "arm-node-2", "selfLink": "/apis/metrics.k8s.io/v1beta1/nodes/arm-node-2", "creationTimestamp": "2018-01-08T15:17:09Z" }, "timestamp": "2018-01-08T15:17:00Z", "window": "1m0s", "usage": { "cpu": "120m", "memory": "614180Ki" } } ] }
Let’s define a HPA that will maintain a minimum of two replicas and will scale up to ten if the CPU average is over 80% or if the memory goes over 200Mi

apiVersion: autoscaling/v2beta1 kind: HorizontalPodAutoscaler metadata: name: podinfo spec: scaleTargetRef: apiVersion: apps/v1beta1 kind: Deployment name: podinfo minReplicas: 2 maxReplicas: 10 metrics: - type: Resource resource: name: cpu targetAverageUtilization: 80 - type: Resource resource: name: memory targetAverageValue: 200Mi

Apply the podinfo HPA:
$ kubectl --kubeconfig terraform output kubectl_config) \ apply -f httpsraw.githubusercontent.com/stefanprodan/k8s-podinfo/master/deploy/auto-scaling/podinfo-hpa.yaml horizontalpodautoscaler "podinfo" created
After a couple of seconds the HPA controller will contact the metrics server and will fetch the CPU and memory usage:
$ kubectl --kubeconfig terraform output kubectl_config) get hpa NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE podinfo Deployment/podinfo 2826240 / 200Mi, 15% / 80% 2 10 2 5m
In order to increase the CPU usage we could run a load test with hey:
#install hey go get -u github.com/rakyll/hey #do 10K requests rate limited at 20 QPS hey -n 10000 -q 10 -c 5 httpterraform output k8s_master_public_ip):31190
You can monitor the autoscaler events with:
$ kubectl --kubeconfig terraform output kubectl_config) describe hpa Events: Type Reason Age From MessageNormal SuccessfulRescale 7m horizontal-pod-autoscaler New size: 4; reason: cpu resource utilization (percentage of request) above target Normal SuccessfulRescale 3m horizontal-pod-autoscaler New size: 8; reason: cpu resource utilization (percentage of request) above target
After the load tests finishes the autoscaler will remove replicas until the deployment reaches the initial replica count:
Events: Type Reason Age From MessageNormal SuccessfulRescale 20m horizontal-pod-autoscaler New size: 4; reason: cpu resource utilization (percentage of request) above target Normal SuccessfulRescale 16m horizontal-pod-autoscaler New size: 8; reason: cpu resource utilization (percentage of request) above target Normal SuccessfulRescale 12m horizontal-pod-autoscaler New size: 10; reason: cpu resource utilization (percentage of request) above target Normal SuccessfulRescale 6m horizontal-pod-autoscaler New size: 2; reason: All metrics below target
 Conclusions
Thanks to kubeadm and Terraform, bootstrapping a Kubernetes cluster on bare-metal can be done with a single command and it takes just ten minutes to have a fully functional setup. If you have any suggestion on improving this guide please submit an issue or PR on GitHub at stefanprodan/k8s-scw-baremetal. Contributions are more than welcome!