This tutorial discusses how to automatically scale your microservices applications running on Kubernetes using Horizontal Pod Autoscaler (HPA) resources. It also discusses how to automatically scale your Kubernetes cluster nodes using Cluster Autoscaler (CA) resource.
One of the big advantages of the cloud is the ability to create intelligent applications that respond to user demand. Applications designed with autoscaling capabilities can scale in or out to meet expected baseline performance. In order words, you can configure your application to use more resources with increased demand and fewer resources with less demand. Properly implemented, autoscaling can save tons of money since the cloud infrastructure is pay-as-you-go. i.e you pay only for the resources that you use.
Kubernetes is generally deployed either on-premise or in the cloud. Kubernetes in the cloud require either using a managed service (e.g AWS EKS) or deployed in the cloud using DIY tools like KOPS or Kubeadm.
In this tutorial, the cluster will be deployed using KOPS. Apart from being a production-grade tool for deploying Kubernetes, KOPs can integrate with your cloud provider and hence can speak the cloud API. This feature is important to achieve Cluster Autoscaling as the cluster autoscaler will need to communicate with the cloud provider to add more resources like virtual machines (EC2 in AWS).
Steps in Configuring HPA for a microservice application:
Cluster Autoscaler monitors the Status of the Kubernetes pods on your cluster. A Pod status is either Pending, Creating or Running. Pending generally means that the Kubernetes scheduler is yet to assign the pod to a node. There could be a couple of reasons for this but one of the obvious reasons for the pod to remain in Pending state is when there are no available Cluster Nodes that meet the requirement to schedule the pod. Cluster Autoscaler uses the Pending state as the condition for triggering the scale-out of additional cluster nodes. Cluster Autoscaler notices this condition and corrects it by creating additional nodes to join the cluster. It will create as many nodes as required to deploy all the Pending pods, not exceeding the Max nodes that you configured. The maximum number of nodes that will be created depends on your Cluster Autoscaler configuration. Once the additional nodes have joined the cluster, it will schedule the Pending pods to the nodes.
In this tutorial we will do the following:
Create the Kubernetes cluster on AWS with KOPS. This requires familiarity with AWS Cloud. Note that the instructions for other cloud providers like Google Cloud and MS Azure will differ slightly. The Architecture of the deployment is shown below. You need to create a deployment VM to deploy KOPS. Once KOPS is deployed, Kops will be instructed to create the cluster. KOPS will create the VMS requited to install Kubernetes and deploy Kubernetes. In the topology below, KOPs deployed Kubernetes on 3 nodes of 1 master and 2 worker nodes. The only VM that we have to create is the deployment VM. KOPS will create an Autoscaling Group in AWS and add the worker Nodes to the Autoscaling Group. In this way, the Cluster Autoscaler can add/remove additional nodes to the Autoscaling Group when needed.
To prepare a cluster with KOPS you need to have the following ready:
Once these are ready, you can export them as follows:
$export DOMAIN_NAME="cte.cloudtechexperts.com" $export CLUSTER_AWS_REGION="us-east-1" $export CLUSTER_AWS_AZ="us-east-1a" $export KOPS_STATE_STORE="s3://cte.cloudtechexperts.com"
Configure your CLI with AWS secret key and secret access key. Then test to ensure that the configuration works. This is important because everything going forward depends on you being able to talk to AWS API from the CLI.
$aws configure $aws s3 ls
Accept all the default values by just pressing the return key.
$ssh-keygen
It’s very important that the versions of KOPs and Kubectl are compatible. You need to contact the compatibility reference pages to decide on the best version to use. Even when you decide to upgrade at a later time, ensure to use compatible versions.
This tutorial uses version 1.13.0 of kubectl, Kubernetes cluster, and KOPS.
$sudo wget https://github.com/kubernetes/kops/releases/download/1.13.0/kops-linux-amd64 $sudo chmod +x kops-linux-amd64 $sudo mv kops-linux-amd64 /usr/local/bin/kops $sudo chmod +x /usr/local/bin/kops $wget https://storage.googleapis.com/kubernetes-release/release/v1.13.0/bin/linux/amd64/kubectl $sudo chmod +x kubectl $sudo mv kubectl /usr/local/bin/
Let’s deploy a 3-node Kubernetes cluster with KOPS. Take note of the version of Kubernetes API used here. Take note of the Instance size for the master node. The minimum tends to be t2.medium. After you run the command, it could take up to 7 minutes for the cluster to be fully ready.
#----
$kops create cluster --name=${DOMAIN_NAME} --zones=${CLUSTER_AWS_AZ} --master-size="t2.medium" --node-size="t2.micro" --node-count="2" --dns-zone=${DOMAIN_NAME} --ssh-public-key="~/.ssh/id_rsa.pub" --kubernetes-version="1.13.0" --yes
#---
When KOPS finishes running, it will display the following:
Cluster is starting. It should be ready in a few minutes. Suggestions: * validate cluster: kops validate cluster * list nodes: kubectl get nodes --show-labels * ssh to the master: ssh -i ~/.ssh/id_rsa admin@api.cte.cloudtechexperts.com * the admin user is specific to Debian. If not using Debian please use the appropriate user based on your OS. * read about installing addons at: https://github.com/kubernetes/kops/blob/master/docs/addons.md.
Wait for a few minutes and check that the cluster is ready.
$kubectl get nodes NAME STATUS ROLES AGE VERSION NAME STATUS ROLES AGE VERSION ip-172-20-38-139.ec2.internal Ready node 67m v1.13.0 ip-172-20-55-157.ec2.internal Ready node 61m v1.13.0 ip-172-20-56-26.ec2.internal Ready master 73m v1.13.0
Hooray, our 3-node cluster is ready for use!
Configure the maximum size for the AWS Autoscaling Group that controls the number of worker nodes. The original size of MaxSize was 2 but change it to 4 or any desired sized to look as shown below:
$kops edit ig nodes
apiVersion: kops/v1alpha2
kind: InstanceGroup
metadata:
creationTimestamp: 2019-09-19T15:06:23Z
generation: 1
labels:
kops.k8s.io/cluster: cte.cloudtechexperts.com
name: nodes
spec:
image: kope.io/k8s-1.13-debian-stretch-amd64-hvm-ebs-2019-08-16
machineType: t2.micro
maxSize: 4
minSize: 2
nodeLabels:
kops.k8s.io/instancegroup: nodes
role: Node
subnets:
- us-east-1a
$kops update cluster ${DOMAIN_NAME} --yes
After this, check that your AWS Autoscaling group has the MAXsize reflected. You can check from the AWS Mangement Dashboard.
Copy the policy from here and ensure to name it policy-cluster-autoscaler.json.
$aws iam put-role-policy --role-name nodes.${DOMAIN_NAME} --policy-name asg-nodes.${DOMAIN_NAME} --policy-document file://policy-cluster-autoscaler.json
Autoscaling requires that you monitor metrics to help determine when to scale. Popular choices for Kubernetes are Prometheus and Metrics-server. Here we use Metrics Server.
$sudo yum install git -y $git clone https://github.com/kubernetes-incubator/metrics-server $cd /home/ec2-user/metrics-server/ $kubectl create -f deploy/1.8+/ clusterrole.rbac.authorization.k8s.io/system:aggregated-metrics-reader created clusterrolebinding.rbac.authorization.k8s.io/metrics-server:system:auth-delegator created rolebinding.rbac.authorization.k8s.io/metrics-server-auth-reader created apiservice.apiregistration.k8s.io/v1beta1.metrics.k8s.io created serviceaccount/metrics-server created deployment.apps/metrics-server created service/metrics-server created clusterrole.rbac.authorization.k8s.io/system:metrics-server created clusterrolebinding.rbac.authorization.k8s.io/system:metrics-server created
At this point the metrics server is up and running: However, we are getting an error since it’s not able to talk to the Kubelet on the worker nodes to get metrics as you can see below.
$kubectl top no error: metrics not available yet $kubectl get pods -n kube-system metrics-server-6456bffb67-d5vrq 1/1 Running 0 20s
The Metrics server is running but we are not able to retrieve metrics from the cluster. To Solve this for the version of Kubernetes deployed with KOPS, we need to make 2 changes:
Need to configure metrics server to allow unsecured tls connection. This is good enough for now but you may investigate how to make metrics server work with tls.
$cd /home/ec2-user/metrics-server/deploy/1.8+/
Edit metrics-server-deployment.yaml to have the containers section look like this:
containers:
- name: metrics-server
image: k8s.gcr.io/metrics-server-amd64:v0.3.4
imagePullPolicy: Always
command:
- /metrics-server
- --kubelet-insecure-tls
- --kubelet-preferred-address-types=InternalIP
Then delete and recreate the metrics-server
$kubectl delete -f metrics-server-deployment.yaml $kubectl create -f metrics-server-deployment.yaml
Makes changes to the Kubernetes cluster. Since we deployed with KOPS utility we will make the changes with KOPS.
$kops edit cluster --name cte.cloudtechexperts.com
Modify the Kubelet section from:
kubelet:
anonymousAuth: false
To
kubelet:
anonymousAuth: false
authenticationTokenWebhook: true
authorizationMode: Webhook
Update the KOPS cluster with the changes and wait for the cluster to be ready. This could take up to 20 minutes so be patient!
$kops update cluster --yes $kops rolling-update cluster --yes
When the Cluster is ready:
$kubectl get nodes NAME STATUS ROLES AGE VERSION ip-172-20-38-139.ec2.internal Ready node 67m v1.13.0 ip-172-20-55-157.ec2.internal Ready node 61m v1.13.0 ip-172-20-56-26.ec2.internal Ready master 73m v1.13.0
Test that the Metrics server is working. You will see the pod running in the Kube-system namespace.
$kubectl get pods -n kube-system metrics-server-6456bffb67-9bb6m 1/1 Running 0 7m14
$kubectl top no NAME CPU(cores) CPU% MEMORY(bytes) MEMORY% ip-172-20-38-139.ec2.internal 29m 2% 381Mi 42% ip-172-20-55-157.ec2.internal 25m 2% 410Mi 45% ip-172-20-56-26.ec2.internal 829m 41% 1354Mi 35%
Download the Cluster Autoscaler YAML file from here or you may just copy and paste the YAML file using your favorite editor like vim. You may name the file cluster-autoscaler.yaml
Make 3 changes to this file:
Ensure that the name of the AWS Autoscaling group is correct. Also, ensure that the minimum and the maximum number of nodes for the Autoscaler corresponds to what you have configured in the AWS autoscaling groups in Step 1 above. The original file downloaded contains the entry
– –nodes=1:10:k8s-worker-asg-1
But we want it to look below because this is what our AWS autoscaling group looks like:
– –nodes:=2:4:nodes.cte.cloudtechexperts.com
We will use the Linux sed tool to make the edit. Alternatively, you, may just open the file and edit the line manually.
$export MIN_NODES="2"
$export MAX_NODES="4"
$sed -i -e "s|--nodes=.*|--nodes:=${MIN_NODES}:${MAX_NODES}:nodes.${DOMAIN_NAME}|g" ./cluster-autoscaler.yaml
Ensure that the certificate path is correct. I changed from line 1 to line 2 below:
/etc/ssl/certs/ca-certificates.crt
/etc/pki/tls/certs/ca-certificates.crt
After the changes, your Autoscaler file should have the Command section look like below. TAKE NOTE of the path of the certificate /etc/pki/tls/certs/ca-certificates.crt and the line starting with — nodes.
command:
- ./cluster-autoscaler
- --v=4
- --stderrthreshold=info
- --cloud-provider=aws
- --skip-nodes-with-local-storage=false
- --nodes=2:4:nodes.cte.cloudtechexperts.com
volumeMounts:
- name: ssl-certs
mountPath: /etc/pki/tls/certs/ca-certificates.crt
readOnly: true
imagePullPolicy: "Always"
volumes:
- name: ssl-certs
hostPath:
path: "/etc/pki/tls/certs/ca-bundle.crt"
Note that the Cluster Autoscaler image version must be compatible with the version of Kubernetes you are using. You can check here for compatibility of the images for your Kubernetes API. For Kubernetes v1.13.0, we are also using Cluster Autoscaler image v1.13.0 as you can see below:
containers:
- image: k8s.gcr.io/cluster-autoscaler:v1.13.1
name: cluster-autoscaler
resources:
limits:
cpu: 100m
memory: 300Mi
requests:
cpu: 100m
$kubectl apply -f cluster-autoscaler.yaml serviceaccount/cluster-autoscaler created clusterrole.rbac.authorization.k8s.io/cluster-autoscaler created role.rbac.authorization.k8s.io/cluster-autoscaler created clusterrolebinding.rbac.authorization.k8s.io/cluster-autoscaler created rolebinding.rbac.authorization.k8s.io/cluster-autoscaler created deployment.apps/cluster-autoscaler created $kubectl get pods -n kube-system NAME READY STATUS RESTARTS AGE cluster-autoscaler-7cfd87ccc-dtqgd 1/1 Running 0 11h dns-controller-79cbf4d696-4cct9 1/1 Running 0 11h etcd-manager-events-ip-172-20-32-68.ec2.internal 1/1 Running 0 11h etcd-manager-main-ip-172-20-32-68.ec2.internal 1/1 Running 0 11h kube-apiserver-ip-172-20-32-68.ec2.internal 1/1 Running 3 11h kube-controller-manager-ip-172-20-32-68.ec2.internal 1/1 Running 0 11h kube-dns-685fbb458-52252 3/3 Running 0 11h kube-dns-685fbb458-dm5gm 3/3 Running 0 11h kube-dns-autoscaler-74887878cc-fqxmp 1/1 Running 0 11h kube-proxy-ip-172-20-32-68.ec2.internal 1/1 Running 0 11h kube-proxy-ip-172-20-39-18.ec2.internal 1/1 Running 0 11h kube-proxy-ip-172-20-39-189.ec2.internal 1/1 Running 0 11h kube-scheduler-ip-172-20-32-68.ec2.internal 1/1 Running 0 11h metrics-server-65d64d6865-2jdql 1/1 Running 0 11h
Above you can see that the cluster-autoscaler pod is running, highlighted in bold.
We will use just hpa-apache image. The kubectl run command creates a deployment, a replica set and the pods required to run this container.
$kubectl run php-apache --image=gcr.io/google_containers/hpa-example --requests=cpu=200m --expose --port=80
Note that this command also creates a service object because of the –expose tag
$ kubectl get svc NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE kubernetes ClusterIP 100.64.0.1 <none> 443/TCP 44m php-apache ClusterIP 100.70.86.121 <none> 80/TCP 30s $kubectl get pods NAME READY STATUS RESTARTS AGE php-apache-864967cc4f-tjz87 1/1 Running 0 26s
The HPA command is the ‘autoscale’ command in the line below. This is what performs the autoscaling of the application. It is using CPU Utilization as the scaling metrics with a Threshold value of 20%. This means that each time the average CPU utilization of all the pods exceeds 20%, more pods will be added. The minimum number of pods to be maintained will be 2 and Maximum will be 20. In production cases autoscaling, 70% is the normal threshold but we use 20% here for demo purposes alone.
$ kubectl autoscale deployment php-apache --cpu-percent=20 --min=2 --max=20 horizontalpodautoscaler.autoscaling/php-apache autoscaled $kubectl get hpa php-apache NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE php-apache Deployment/php-apache <unknown>/20% 2 20 0 12s $ kubectl get hpa php-apache NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE php-apache Deployment/php-apache 0%/20% 2 20 2 55s
In Kubernetes, the Service object acts as the frontend load balancer to the application and you access an application through the Service object. We will create a pod using busybox image, ssh into the image and run an infinite loop sending traffic to the service object. This will saturate the backend pods connected to the service object hence increasing the CPU Utilization of the pods.
$kubectl run -i --tty load-generator --image=busybox /bin/sh # while true; do wget -q -O- http://php-apache.default.svc.cluster.local; done
Login from another terminal to the KOPS master node on AWS. This is so as to leave the load generator running on the previous terminal.
$ssh -i k8skeyclass.pem ec2-user@3.88.132.232 $watch kubectl get hpa NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE php-apache Deployment/php-apache 0%/20% 2 20 2 11h NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE php-apache Deployment/php-apache 236%/20% 2 20 2 3m51s NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE php-apache Deployment/php-apache 236%/20% 2 20 4 4m10s NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE php-apache Deployment/php-apache 236%/20% 2 20 8 4m23s NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE php-apache Deployment/php-apache 236%/20% 2 20 16 4m31s NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS AGE php-apache Deployment/php-apache 123%/20% 2 20 20 4m46s
More pods go into ‘Pending’ state as 2 Cluster node is not sufficient to hosts all the Pods.
[ec2-user@ip-172-31-92-153 ~]$ kubectl get pods NAME READY STATUS RESTARTS AGE load-generator-557649ddcd-r4ktb 1/1 Running 0 3m19s php-apache-864967cc4f-28bqg 1/1 Running 0 92s php-apache-864967cc4f-2xl22 1/1 Running 0 5m1s php-apache-864967cc4f-2zj6d 0/1 Pending 0 77s php-apache-864967cc4f-6mncl 0/1 Pending 0 62s php-apache-864967cc4f-bgs2h 0/1 Pending 0 62s php-apache-864967cc4f-chhh9 0/1 Pending 0 62s php-apache-864967cc4f-fmzdn 0/1 Pending 0 62s php-apache-864967cc4f-jgh55 0/1 Pending 0 47s php-apache-864967cc4f-kcn9k 1/1 Running 0 6m51s php-apache-864967cc4f-kvm8l 0/1 Pending 0 47s php-apache-864967cc4f-kzqgb 0/1 Pending 0 62s php-apache-864967cc4f-l7dtb 0/1 Pending 0 62s php-apache-864967cc4f-lqw8n 1/1 Running 0 92s php-apache-864967cc4f-ndjdp 0/1 Pending 0 62s php-apache-864967cc4f-p7kv4 0/1 Pending 0 77s php-apache-864967cc4f-pwh9q 0/1 Pending 0 77s php-apache-864967cc4f-r49gk 0/1 Pending 0 47s php-apache-864967cc4f-tftpl 0/1 Pending 0 47s php-apache-864967cc4f-wfqnc 0/1 Pending 0 77s php-apache-864967cc4f-zhjkq 0/1 Pending 0 62s
Cluster Autoscaler then scales the number of worker nodes from 2 to 4 as you can see below.
[ec2-user@ip-172-31-92-153 ~]$ kubectl get nodes NAME STATUS ROLES AGE VERSION ip-172-20-35-155.ec2.internal Ready node 21m v1.13.0 ip-172-20-37-80.ec2.internal Ready node 35s v1.13.0 ip-172-20-43-242.ec2.internal Ready node 15m v1.13.0 ip-172-20-44-99.ec2.internal Ready node 38s v1.13.0 ip-172-20-57-10.ec2.internal Ready master 28m v1.13.0
Since more capacity, more pods are rescheduled to the new nodes and containers start creating.
[ec2-user@ip-172-31-92-153 ~]$ kubectl get pods NAME READY STATUS RESTARTS AGE load-generator-557649ddcd-r4ktb 1/1 Running 0 3m44s php-apache-864967cc4f-28bqg 1/1 Running 0 117s php-apache-864967cc4f-2xl22 1/1 Running 0 5m26s php-apache-864967cc4f-2zj6d 0/1 Pending 0 102s php-apache-864967cc4f-6mncl 0/1 ContainerCreating 0 87s php-apache-864967cc4f-bgs2h 0/1 Pending 0 87s php-apache-864967cc4f-chhh9 0/1 ContainerCreating 0 87s php-apache-864967cc4f-fmzdn 0/1 ContainerCreating 0 87s php-apache-864967cc4f-fpgjv 0/1 Pending 0 9s php-apache-864967cc4f-jgh55 0/1 ContainerCreating 0 72s php-apache-864967cc4f-kcn9k 1/1 Running 0 7m16s php-apache-864967cc4f-kvm8l 0/1 Pending 0 72s php-apache-864967cc4f-kzqgb 0/1 OutOfcpu 0 87s php-apache-864967cc4f-l7dtb 0/1 ContainerCreating 0 87s php-apache-864967cc4f-lqw8n 1/1 Running 0 117s php-apache-864967cc4f-ndjdp 0/1 OutOfcpu 0 87s php-apache-864967cc4f-p7kv4 0/1 ContainerCreating 0 102s php-apache-864967cc4f-pwh9q 0/1 ContainerCreating 0 102s php-apache-864967cc4f-r49gk 0/1 Pending 0 72s php-apache-864967cc4f-tftpl 0/1 Pending 0 72s php-apache-864967cc4f-wfqnc 0/1 Pending 0 102s php-apache-864967cc4f-zb88l 0/1 Pending 0 6s php-apache-864967cc4f-zhjkq 0/1 ContainerCreating 0 87s
Now the Cluster Nodes stabilizes at 4, our MaxSize for the worker nodes.
[ec2-user@ip-172-31-92-153 ~]$ kubectl get nodes NAME STATUS ROLES AGE VERSION ip-172-20-35-155.ec2.internal Ready node 21m v1.13.0 ip-172-20-37-80.ec2.internal Ready node 35s v1.13.0 ip-172-20-43-242.ec2.internal Ready node 15m v1.13.0 ip-172-20-44-99.ec2.internal Ready node 38s v1.13.0 ip-172-20-57-10.ec2.internal Ready master 28m v1.13.0
And the number of Pods also stabilizes, though some pods are still in the Pending state but we set the maximum cluster nodes to 4 so no more nodes will be added.
[ec2-user@ip-172-31-92-153 ~]$ kubectl get pods NAME READY STATUS RESTARTS AGE load-generator-557649ddcd-r4ktb 1/1 Running 0 4m37s php-apache-864967cc4f-28bqg 1/1 Running 0 2m50s php-apache-864967cc4f-2xl22 1/1 Running 0 6m19s php-apache-864967cc4f-2zj6d 0/1 Pending 0 2m35s php-apache-864967cc4f-6mncl 1/1 Running 0 2m20s php-apache-864967cc4f-bgs2h 0/1 Pending 0 2m20s php-apache-864967cc4f-chhh9 1/1 Running 0 2m20s php-apache-864967cc4f-fmzdn 1/1 Running 0 2m20s php-apache-864967cc4f-fpgjv 0/1 Pending 0 62s php-apache-864967cc4f-jgh55 1/1 Running 0 2m5s php-apache-864967cc4f-kcn9k 1/1 Running 0 8m9s php-apache-864967cc4f-kvm8l 0/1 Pending 0 2m5s php-apache-864967cc4f-kzqgb 0/1 OutOfcpu 0 2m20s php-apache-864967cc4f-l7dtb 1/1 Running 0 2m20s php-apache-864967cc4f-lqw8n 1/1 Running 0 2m50s php-apache-864967cc4f-ndjdp 0/1 OutOfcpu 0 2m20s php-apache-864967cc4f-p7kv4 1/1 Running 0 2m35s php-apache-864967cc4f-pwh9q 1/1 Running 0 2m35s php-apache-864967cc4f-r49gk 0/1 Pending 0 2m5s php-apache-864967cc4f-tftpl 0/1 Pending 0 2m5s php-apache-864967cc4f-wfqnc 0/1 Pending 0 2m35s php-apache-864967cc4f-zb88l 0/1 Pending 0 59s php-apache-864967cc4f-zhjkq 1/1 Running 0 2m20s
Let’s stop the load generator:
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Now that the load has been reduced, the cluster should scale down to 2 nodes.
In this tutorial, I have shown you how to autoscale your microservices application running on Kubernetes as well as how to scale your Kubernetes cluster. To successfully follow this tutorial, you require good knowledge of the AWS cloud and a good knowledge of the Kubernetes platform and its ecosystem. That being said, if you follow carefully, you will be able to get this up and running. I hope you enjoy this and feel free to share it with colleagues.
