Multi and Hybrid cloud deployments have become more of a norm than exception and how seamlessly you can manage resources deployed across multiple environments would determine the success of your digital transformation. Azure Arc enables this by providing a solution that enables consistent management of workloads across environments. It helps onboard resources from heterogeneous deployments and manage them using familiar premises of Azure Resource Manager. Azure Arc currently supports VM, Kubernetes clusters(preview) and databases(preview) , and you can monitor and manage them from Azure irrespective of where it is deployed.
Azure Arc can be used for centralized monitoring and management of k8s clusters deployed across different cloud environments or on-premises. This service is currently in preview. As part of my weekend tinkering , I explored Azure Arc enabled Kubernetes cluster. The process for setting it up for a lab is pretty straight forward, and you will get most of this information from publicly available documents. I have made few tweaks to get them to suit the k8s clusters that I created
To start with, you need to get the kubeconfig file of the cluster that should be integrated with Azure Arc. For testing purpose I created a k8s cluster for testing the integration though kubeadm. That was an interesting experiment in itself as the deployment was done in an Azure VM. The steps to be followed are based on the following article: https://www.mirantis.com/blog/how-install-kubernetes-kubeadm/ . However to make the cluster accessible over a public DNS, some additional configuration was required. For instance, the kubeadm deployment exposes the API server over port 6443. So inbound connection to this port has to be enabled in the NSG of the VM.
My tweaks for to get Kubeadm based cluster deployment working in Azure in addition to the steps mentioned in the document are as follows
1. Deploy an Ubuntu 18 machine from marketplace
2. Create a DNS entry for the VM and map it to the public IP.
3. Create NGS with that allows inbound connection at port 6443 from internet in addition to the default SSH port
4. Use the DNS name of the VM in the kubeadm init command while creating the cluster. Else the certificate will not be bound to the DNS and you will not be able to access the cluster from external and add it to AzureArc. Sample command I used is given below
kubeadm init --pod-network-cidr=192.168.0.0/16 --control-plane-endpoint kubeadmclstr.eastus2.cloudapp.azure.com
5. Calico installation should be done using the following steps
curl https://docs.projectcalico.org/manifests/calico.yaml -O
kubectl apply -f calico.yaml
You can check out the below video for the full installation process
Now our K8s cluster is created using kubeadm. Copy/upload the kubeconfig file to the environment from where you are configuring AzureArc integration. I configured the AzureArc intergation from cloudshell, hence uploaded kubeconfig file to be Azure CloudShell session. Follow this document to enable integration with AzureArc: https://docs.microsoft.com/en-us/azure/azure-arc/kubernetes/connect-cluster
I have recorded a video of the integration process, you can refer to it below.
There my k8s cluster is listed in AzureArc!! Now if you want to get additional insights into your cluster performance, enable monitoring of the cluster using the steps in the MS document. Its easy-peasy really, you can simply follow through the document.
I tried it for one of my AzureArc enabled clusters , you can refer the video below to view the process .
Note: I enabled monitoring through Bash and integrated my k8s cluster with an existing log analytics work space. For automated CI/CD deployments , you can also use service principals as described in the doc
Viola, Now I can view my K8s cluster and view the associated metrics & logs directly from Azure portal. Of course in real world, this would be your production k8s clusters. As the service is now in preview, you can use it for test and dev purposes and not in production. Hope this blog + videos will help you get started with that. Happy learning!!