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.
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
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
OR
To generate a new SSH Keys while creating the AKS cluster use the following command
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.
Add a on-demand based node pool to the cluster and add a label deploy=ondemand
Configure kubectl to connect to the newly created Kubernetes cluster
Let's confirm that the nodes in the spot and on-demand node pools are distributed across all availability zones evenly.
This should output the list of Nodes and their corresponding availability zones as shown below
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
It will crate two deployments and two services one each for frontend and backend as shown below
Let's check the list pods and which node it's been deployed currently using the following kubectl command
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.
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.
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.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.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.
Which will update only the frontend deployment as we changed only that configuration
Now let's look at the list of pods to identify which host it's being placed using the following command
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.
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.
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