Optimize Azure Kubernetes Service Node Cost by Combining OnDemand And Spot VMs

While it's possible to run the Kubernetes nodes either in on-demand or spot node pools separately, we can optimize the application cost without compromising the reliability by placing the pods unevenly on spot and OnDemand VMs using the topology spread constraints. With baseline amount of pods deployed in OnDemand node pool offering reliability, we can scale on spot node pool based on the load at a lower cost.
Kubernetes Topology Spread

In this post, we will go through a step by step approach on deploying an application spread unevenly on spot and OnDemand VMs.

Prerequisites

Azure Subscription with permissions to create the required resources
Azure CLI
kubectl CLI

1. Create a Resource Group and an AKS Cluster

Create a resource group in your preferred Azure location using Azure CLI as shown below

az group create --name CostOptimizedK8sRG --location westeurope --tags 'Reason=Blog'

Let's create an AKS cluster using one of the following commands.

If you already have your own SSH keys use the following command to use the existing keys

az aks create -g CostOptimizedK8sRG -n CostOptimizedCluster --auto-upgrade-channel node-image --enable-managed-identity --enable-msi-auth-for-monitoring --enable-cluster-autoscaler --min-count 1 --max-count 5 --kubernetes-version 1.26.0 --ssh-key-value ~/.ssh/id_rsa.pub --tags 'Reason=Blog' --uptime-sla -z 1 2 3

To generate a new SSH Keys while creating the AKS cluster use the following command

az aks create -g CostOptimizedK8sRG -n CostOptimizedCluster --auto-upgrade-channel node-image --enable-managed-identity --enable-msi-auth-for-monitoring --enable-cluster-autoscaler --min-count 1 --max-count 5 --generate-ssh-keys --kubernetes-version 1.26.0 --tags 'Reason=Blog' --uptime-sla -z 1 2 3

2. Create two node pools using spot and OnDemand VMs

Add a spot based node pool to the cluster and add a label deploy=spot. We set the spot max price to that of OnDemand cost.

az aks nodepool add -g CostOptimizedK8sRG --cluster-name CostOptimizedCluster -n appspotpool -e -k 1.26.0 --labels 'deploy=spot' --min-count 3 --max-count 5 --max-pods 10 --mode User --os-sku Ubuntu --os-type Linux --priority Spot --spot-max-price -1 --tags 'Reason=Blog' -z 1 2 3

Add a on-demand based node pool to the cluster and add a label deploy=ondemand

az aks nodepool add -g CostOptimizedK8sRG --cluster-name CostOptimizedCluster -n appondempool -e -k 1.26.0 --labels 'deploy=ondemand' --min-count 3 --max-count 5 --max-pods 10 --mode User --os-sku Ubuntu --os-type Linux --priority Regular --tags 'Reason=Blog' -z 1 2 3

Configure kubectl to connect to the newly created Kubernetes cluster

az aks get-credentials -g CostOptimizedK8sRG -n CostOptimizedCluster

Let's confirm that the nodes in the spot and on-demand node pools are distributed across all availability zones evenly.

kubectl get nodes -o custom-columns='Name:.metadata.name,Zone:.metadata.labels.topology\.kubernetes\.io/zone'

This should output the list of Nodes and their corresponding availability zones as shown below

Name Zone aks-appondempool-79574777-vmss000000 westeurope-1 aks-appondempool-79574777-vmss000001 westeurope-2 aks-appondempool-79574777-vmss000002 westeurope-3 aks-appspotpool-41295273-vmss000000 westeurope-1 aks-appspotpool-41295273-vmss000001 westeurope-2 aks-appspotpool-41295273-vmss000002 westeurope-3 aks-nodepool1-17327460-vmss000000 westeurope-1

3. Deploy a sample application

Now let's deploy a sample application Vote App as shown in this documentation. Copy the application manifest in the documentation to a file named azure-vote.yaml and apply the manifest using kubectl as shown below

kubectl apply -f azure-vote.yaml

It will crate two deployments and two services one each for frontend and backend as shown below

deployment.apps/azure-vote-back created service/azure-vote-back created deployment.apps/azure-vote-front created service/azure-vote-front created

Let's check the list pods and which node it's been deployed currently using the following kubectl command

kubectl get pods -o wide

As you notice that there is one pod each of frontend and backend. Both of them are running OnDemand node pool because we haven't added tolerations to support Spot taint.

NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES azure-vote-back-65c595548d-249xw 1/1 Running 0 90s 10.244.9.4 aks-appondempool-79574777-vmss000002 <none> <none> azure-vote-front-d99b7676c-2nvg2 1/1 Running 0 90s 10.244.11.4 aks-appondempool-79574777-vmss000000 <none> <none>

4. Update the application deployment using topology spread constraints

Let's update the number of replicas of front end application to 9. I have chosen 9 to have a better distribution of pods across 3 Availability Zones where the nodes are running. We need to add tolerations to the deployment manifest to enable the possibility of deploying the pods in spot VMs.

tolerations: - key: kubernetes.azure.com/scalesetpriority operator: Equal value: spot effect: NoSchedule

Now let's use Node affinity to constrain which nodes that scheduler can use to place the pods. Using requiredDuringSchedulingIgnoredDuringExecution we ensure that the pods are placed in nodes which has deploy as key and the value as either spot or ondemand. Whereas for preferredDuringSchedulingIgnoredDuringExecution we will add weight such that spot nodes has more preference over OnDemand nodes for the pod placement.

affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: deploy operator: In values: - spot - ondemand preferredDuringSchedulingIgnoredDuringExecution: - weight: 99 preference: matchExpressions: - key: deploy operator: In values: - spot - weight: 1 preference: matchExpressions: - key: deploy operator: In values: - ondemand

With Tolerations & Node Affinity in place now let's configure topologySpreadConstraints with two label selectors. One with deploy label as topology key, the attribute maxSkew as 3 and DoNotSchedule for whenUnsatisfiable which ensures that not less than 3 instances (as we use 9 replicas) will be in single topology domain (in our case spot and ondemand). As the nodes with spot as value for deploy label has the higher weight preference in node affinity, scheduler will most likely will place more pods on spot than OnDemand node pool. For the second label selector we use topology.kubernetes.io/zone as the topology key to evenly distribute the pods across availability zones, as we use ScheduleAnyway for whenUnsatisfiable scheduler won't enforce this distribution but attempt to make it if possible.

topologySpreadConstraints: - labelSelector: matchLabels: app: azure-vote-front maxSkew: 3 topologyKey: deploy whenUnsatisfiable: DoNotSchedule - labelSelector: matchLabels: app: azure-vote-front maxSkew: 1 topologyKey: topology.kubernetes.io/zone whenUnsatisfiable: ScheduleAnyway

Now let's apply the updated manifest to deploy the 9 replicas based on topology spread that we configured above. Full manifest is available this gist.

kubectl apply -f azure-vote.yaml

Which will update only the frontend deployment as we changed only that configuration

deployment.apps/azure-vote-back unchanged service/azure-vote-back unchanged deployment.apps/azure-vote-front configured service/azure-vote-front unchanged

Now let's look at the list of pods to identify which host it's being placed using the following command

kubectl get pods -o wide -l=app=azure-vote-front

As you notice below, 3 pods are placed in OnDemand node pool while 6 pods are placed in spot node pool and all the pods are evenly distributed across the three availability zones.

NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES azure-vote-front-97b44f89b-627md 1/1 Running 0 4m37s 10.244.9.8 aks-appondempool-79574777-vmss000002 <none> <none> azure-vote-front-97b44f89b-66878 1/1 Running 0 100s 10.244.6.6 aks-appspotpool-41295273-vmss000001 <none> <none> azure-vote-front-97b44f89b-68tn6 1/1 Running 0 100s 10.244.8.6 aks-appspotpool-41295273-vmss000000 <none> <none> azure-vote-front-97b44f89b-79gz6 1/1 Running 0 100s 10.244.10.7 aks-appondempool-79574777-vmss000001 <none> <none> azure-vote-front-97b44f89b-7kjzz 1/1 Running 0 100s 10.244.9.9 aks-appondempool-79574777-vmss000002 <none> <none> azure-vote-front-97b44f89b-gvlww 1/1 Running 0 100s 10.244.8.4 aks-appspotpool-41295273-vmss000000 <none> <none> azure-vote-front-97b44f89b-jwwgk 1/1 Running 0 100s 10.244.8.5 aks-appspotpool-41295273-vmss000000 <none> <none> azure-vote-front-97b44f89b-mf84z 1/1 Running 0 100s 10.244.7.4 aks-appspotpool-41295273-vmss000002 <none> <none> azure-vote-front-97b44f89b-p8sxw 1/1 Running 0 100s 10.244.6.5 aks-appspotpool-41295273-vmss000001 <none> <none>

Conclusion

We have seen how we can use topology spread constraints to distribute the pods across the node pools in a cluster. We have also seen how we can use node affinity to constrain the nodes that scheduler can use to place the pods. We have also seen how we can use tolerations to enable the possibility of deploying the pods in spot VMs.

While this approach thrives to achieve optimal placement, it doesn't guarantee that the maximum number of pods will be in spot VMs. This is because the scheduler will try to place the pods in the nodes with higher weight with preference, but it's not required to do so. Similarly the maxSkew configuration in topology spread constraints is the maximum skew allowed as the name suggests, so it's not guaranteed that the maximum number of pods will be in a single topology domain. However, this approach is a good starting point to achieve optimal placement of pods in a cluster with multiple node pools.

As a next step, to improve the spot VM usage we can create additional node pools with different instance types and use node affinity to constrain the pods to be placed in those node pools. This will help us to achieve better spot VM usage.

Published on: February 24, 2023

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Optimize Azure Kubernetes Service Node Cost by Combining OnDemand And Spot VMs

Prerequisites

1. Create a Resource Group and an AKS Cluster

2. Create two node pools using spot and OnDemand VMs

3. Deploy a sample application

4. Update the application deployment using topology spread constraints

Conclusion

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