You have several AI applications that use an Azure Kubernetes Service (AKS) cluster. The cluster supports a maximum of 32 nodes. You discover that occasionally and unpredictably, the application…

The correct answer is B. cluster autoscaler. To keep up with application demands in Azure Kubernetes Service (AKS), you may need to adjust the number of nodes that run your workloads. The cluster autoscaler component can watch for pods in your cluster that can't be scheduled because of resource constraints. When issues…

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Question

You have several AI applications that use an Azure Kubernetes Service (AKS) cluster. The cluster supports a maximum of 32 nodes. You discover that occasionally and unpredictably, the application requires more than 32 nodes. You need to recommend a solution to handle the unpredictable application load. Which scaling method should you recommend?

Options

Ahorizontal pod autoscaler
Bcluster autoscaler
Cmanual scaling
DAzure Container Instances

How the community answered

(40 responses)

A
13% (5)
B
80% (32)
C
3% (1)
D
5% (2)

Explanation

To keep up with application demands in Azure Kubernetes Service (AKS), you may need to adjust the number of nodes that run your workloads. The cluster autoscaler component can watch for pods in your cluster that can't be scheduled because of resource constraints. When issues are detected, the number of nodes is increased to meet the application demand. Nodes are also regularly checked for a lack of running pods, with the number of nodes then decreased as needed. This ability to automatically scale up or down the number of nodes in your AKS cluster lets you run an efficient, cost-effective cluster. https://docs.microsoft.com/en-us/azure/aks/cluster-autoscaler

Topics

#AKS scaling#cluster autoscaler#Kubernetes#unpredictable load

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