WilliamLam.com

Complete vSphere with Tanzu homelab with just 32GB of memory!

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Since the release of vSphere 7.0 Update 1, the demand and interests from the community on getting hands on with vSphere with Tanzu and the new simplified networking solution, has been non-stop. Most folks are either upgrading their existing homelab or looking to purchase new hardware that can better support the new features of the vSphere 7.0 release.

Although vSphere with Tanzu now has a flavor that does not require NSX-T which helps reduces the barrier on getting started, it still has some networking requirements which may not be easily met in for all lab environments. In fact, this was actually the primary reason I had started to look into this since my personal homelab network is very basic and I do not have nor want a switch that can support multiple VLANs, which is one of the requirements for vSphere with Tanzu.

While investigating for a potential solution, which included way too MANY hours of debugging and troubleshooting, I also thought about the absolute minimal amount of resources I could get away with after put everything together. To be clear, my homelab is comprised of a single Supermicro E200-8D which has 128GB of memory and that has served me well over the years and I highly recommend it for anyone that can fit that into their budget. With that said, I did set out with a pretty aggressive goal of using something that is pretty common in VMware homelabs which is an Intel NUC and with just 32GB of memory.

Here is the hardware BOM (similar hardware should also work):

  • Intel NUC 10i7FNH
  • 32GB memory
  • Single 250GB M.2 NVMe SSD
    • NUC can support two SSD (M.2 + SATA), you can always go larger

Here is the software BOM:

Note: The Intel NUCs (Gen 6 to 10) can all support up to 64GB of memory and this is one of the best upgrades you can give yourself, but if you only have 32GB of memory, this will also work.

The final solution will comprise of the following:

  • 1 x vCenter Server Appliance (VCSA) running on the Intel NUC self-managing the ESXi host
  • VMFS storage will be used instead of vSAN to reduce memory footprint (If you have 64GB of memory, recommend using vSAN)
  • Onboard NIC will be used for all traffic and will be attached to a Distributed Virtual Switch (VDS)
    • 3 x Distributed Portgroups will be configured on top of your existing LAN network, the latter two will be routed through our Photon OS Router VM
      • Management – Existing LAN network
      • Frontend – 10.10.0/24
      • Workload – 10.20.0.0/24
  • 1 x vSphere with Tanzu Cluster enabled with Workload Management
  • 1 x HAProxy VM deployed using 3-NIC configuration
  • 1 x Photon OS Linux VM used as a Router for IP forwarding and optionally, a DNS server if you do not already have one
  • 9 x IP Addresses in total will be required from your local LAN network
    • 4 x IP Addresses which should map to following hostnames or similiar
      • esxi-01.tanzu.local
      • vcsa.tanzu.local
      • router.tanzu.local
      • haproxy.tanzu.local
    • 5 x IP Addresses in a consecutive block (e.g. 192.168.30.20-192.168.30.25) will be needed for the Supervisor Control Plane VMs


As part of this solution, I have automated as much of the tasks as possible and all scripts used for this solution can be found at https://github.com/lamw/vsphere-with-tanzu-homelab-scripts which I will be referencing throughout the instructions. There are also a number of techniques and tricks I am using to be able to reduce the overall memory footprint for setting up vSphere with Tanzu, obviously these should not be used in a Production grade environment.

I also want to give a huge thanks to Timo Sugliani for all of his help with the networking question/challenges and Mayank B. from the vSphere with Tanzu Engineering team who helped with the debugging and ultimately making this solution a possibility.
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Configure network proxy using YTT with Tanzu Kubernetes Grid (TKG)

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I was doing some work with Tanzu Kubernetes Grid (TKG) 1.2 using my TKG Demo Appliance Fling and the environment that I was working in did not have direct internet access, which is usually the case for most Production environment. I needed to have outbound connectivity from the TKG Worker Nodes so that they could pull down a set of containers as part of attaching to our Tanzu Mission Control (TMC) service.

Luckily, there was an HTTP proxy server that I could use for this connectivity and we just need to update our TKG templates so the TKG worker nodes will have the proxy settings. In the past, when needing to apply such customizations such as adding a network proxy to TKG, it meant I had to manually edit the TKG Dev/Prod YAML files. As previously shared, Tanzu Kubernetes Grid (TKG) 1.2 now uses the YAML Templating Tool (YTT) tool for customizing TKG plans.

Although the TKG documentation provides an example for YTT template example, it did not actually cover the TKG Worker Nodes which is what I needed but also that I needed to add a command into the postKubeadmCommands for the network proxy to be activated. The issue is that this section no longer exists in the base template like it did in previous versions of TKG and required some additional YTT annotation to get this working.

Here is the complete working ~/.tkg/providers/infrastructure-vsphere/ytt/proxy_nameserver.yaml template that adds the respective HTTP(S) proxy server and No Proxy settings.

Custom Virtual Machine Class Types with vSphere with Tanzu

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When you deploy a Tanzu Kubernetes Grid (TKG) Cluster using the integrated TKG Service in vSphere with Tanzu, you can specify a Virtual Machine Class Type which determines the amount of CPU and Memory resources that are allocated for both the Control Plane and/or Worker Node VMs for your TKG Cluster.

Here is a sample YAML specification that uses the best-effort-xsmall VM class type for both Control Plane and Worker Node, but you can certainly override and choose different classes based on your requirements.

Today, the are a total of 16 VM Class types that you can select from, however these are not customizable which is something that has been coming up more recently. The vSphere with Tanzu team is aware of this request and is working on a solution that not only makes customizing CPU and Memory easier but also supporting storage customization. As you can see from the table below, 16GB is only supported configuration today.


In the mean time, if you need a supported path for customizing your TKG Guest Clusters, one option is to use the TKG Standalone / MultiCloud CLI, which can be used with a vSphere with Tanzu Cluster. You will need to deploy an additional TKG Management Cluster (basically a few VMs), but once you have that, you can override CPU, Memory and Storage of both the Control Plane and Worker Nodes using the following environment variables:

  • VSPHERE_WORKER_NUM_CPUS
  • VSPHERE_WORKER_MEM_MIB
  • VSPHERE_WORKER_DISK_GIB
  • VSPHERE_CONTROL_PLANE_NUM_CPUS
  • VSPHERE_CONTROL_PLANE_MEM_MIB
  • VSPHERE_CONTROL_PLANE_DISK_GIB

If you are interested, the easiest way to get started is by using my TKG Demo Appliance Fling which was just recently updated to the latest TKG 1.2 release which has support for K8s v1.19 which is currently not available on vSphere with Tanzu.

Now, you might ask, would it be possible to create your own custom VM class types using vSphere with Tanzu? Well …. keep reading to find out 🙂

Disclaimer: This is not officially supported by VMware, use at your own risk. These custom changes can potentially impact upgrades or automatically be reverted upon the next update or upgrade. You have been warned.

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