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Automating Workload Management on vSphere with Tanzu

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As promised, here is the complimentary solution to my existing Automated vSphere with Tanzu Lab Deployment Script, which will automatically deploy and configure the required infrastructure (vCenter Server Appliance, ESXi, vSAN and HAProxy VMs) so that you can quickly jump to enabling Workload Management on your vSphere Cluster.

FYI: Ben Corrie, one of the Engineers on the vSphere with Tanzu team recently published a vSphere with Tanzu 4-Part Deep Dive video series where he walks you through in deploying everything from scratch along with the concepts that should help you better understand how vSphere with Tanzu works. He is actually doing this in his own personal homelab and thought this might be useful to share with others. Kudos Ben and highly recommend folks check out his video if you new to vSphere with Tanzu and Kubernetes.


Enabling Workload Management is a manual step after the automated deployment script and as you know, I prefer to automate as much as I can. I have updated my existing PowerCLI Workload Management Module to now also support the new vSphere with Tanzu capability using HAProxy for networking instead of NSX-T. The module can be downloaded from PowerShell Gallery by simply running

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Customizing Kubernetes cluster template (Dev/Prod) plans in Tanzu Kubernetes Grid 1.2

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With previous releases of Tanzu Kubernetes Grid (TKG), if you needed to apply special OS customizations that were applied to the deployed Control Plane and Worker Node VMs, such as injecting commands to handle network proxy or dealing with insecure container registry, your only option was to hand edit the default TKG Dev/Prod YAML templates. Not only was this error prone but because the templates can change from each release, it was difficult to manage and test until you attempted a deployment.

One of the newest features with the release of TKG 1.2 is official support for customizing the Kubernetes (K8s) Cluster Templates Plans using YTT (YAML Templating Tooling) which allows users to provide custom data that can then be patched/overlay to an existing YAML file. YTT itself is part of a larger toolset for building, creating and configuring deployments for K8s called Carvel. The Domain Specific Language (DSL) that YTT uses was not exactly intuitive but since the official TKG documentation had an example to start with, I was able to mostly figure my way through along with some tips from the #carvel Slack channel.

So what was I trying to do? I was working on updating my TKG Demo Appliance Fling to the latest 1.2 release and part of the setup required adding an entry to /etc/hosts file on all TKG VMs that are deployed. Instead of directly messing with the YAML templates, there is now a new "overlay" YAML file in ~/.tkg/providers/infrastructure-vsphere/ytt/vsphere-overlay.yaml which can be used to make such changes.

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Kubernetes on ESXi-Arm using k3s

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The tiny form factor of a Raspberry Pi (rPI) is a fantastic hardware platform to start playing with the ESXi-Arm Fling. You can already do a bunch of fun VMware things like running a lightweight vSAN Witness Node to setting up basic automation environment for PowerCLI, Terraform and Packer to running rPI OS as VM, enabling some neat use cases like consolidating your physical rPI assets which might be running RetroPi and Pi-Hole which many home labbers are doing.

In addition to VMware solutions, its is also a great platform to learn and tinker with new technologies like Kubernetes (K8s) which I am sure many of you have been hearing about 🙂 Although our vSphere with Tanzu and Tanzu Kubernetes Grid (TKG) does not currently work with the ESXi-Arm Fling, I have actually been meaning to try out a super lightweight K8s distribution designed for IoT/Edge called k3s (pronounced k-3-s) which also recently joined Cloud Native Computing Foundation (CNCF) Sandbox level.

k3s is supported on rPI and you normally would have multiple rPI devices to represent the number of nodes, for example if you want a basic 3-Node cluster, you would need three physical rPI devices. With ESXi-Arm, you can now create these nodes as VM, using just a single rPI. This opens up the door for all sorts of explorations, you can create HA cluster or try out more advanced features which might be more difficult if you needed several physical devices. If you mess up, you can simply re-deploy the VM without much pain or simply clone the VM.

In my setup, I am using 3 x Photon OS VMs. One for the primary node and two for k3s worker nodes. You can certainly install k3s on any other Arm-based OS including rPI OS (which can now run as a VM as mentioned earlier).


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