{"id":32913,"date":"2024-05-30T08:01:04","date_gmt":"2024-05-30T06:01:04","guid":{"rendered":"https:\/\/www.dbi-services.com\/blog\/?p=32913"},"modified":"2024-05-30T08:11:17","modified_gmt":"2024-05-30T06:11:17","slug":"investigative-look-into-cloud-native-hyper-converged-infrastructure-with-harvester-2","status":"publish","type":"post","link":"https:\/\/www.dbi-services.com\/blog\/investigative-look-into-cloud-native-hyper-converged-infrastructure-with-harvester-2\/","title":{"rendered":"Investigative look into cloud-native hyper-converged infrastructure with Harvester (2)"},"content":{"rendered":"\n

In the previous episode<\/a>, we took a look together at an overview of Harvester<\/a>. We looked at its components and explored the concepts behind Harvester, its cloud-native architecture, and the installation process. Now, let’s delve deeper into the main purpose of it: scheduling your workload. Let’s start!<\/p>\n\n\n\n

Note: we expanded the Harvester cluster to three nodes. I would like to play with a bit of scheduling, so let’s add two worker nodes.<\/p>\n\n\n\n

Virtual machine provisioning<\/h2>\n\n\n\n

Practical experience is the most effective method to grasp a concept, process, or product. I suggest that we proceed in the same manner.<\/p>\n\n\n\n

As part of a larger consolidation and modernization project, imagine we just received the request to integrate a Microsoft Windows Server 2022 application into our new infrastructure. If you ever wonder how to control the quality of your data, there is, at dbi services, OMrun<\/a>. OMrun is a Windows based application that includes test steps, scenarios, and of course, data adaptors. Let’s take this application as a workload example. <\/p>\n\n\n\n

We are not going to automate anything here, as exploring the application is the main focus, so expect provisioning the virtual machine in a wizard based manner.<\/p>\n\n\n\n

Log into Harvester web interface by entering the IP mentioned in the console of your node. Take the cluster URL, not the node’s IP.<\/p>\n\n\n\n

In order to provision and run a Windows based application, you need first to add the ISO file of the operating system. I’ve already downloaded the file (using the following URL<\/a> on the Microsoft web site).<\/p>\n\n\n\n

For this, left-hand menu, select Images and then Create.<\/p>\n\n\n\n

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Upload the file and select Create.<\/p>\n\n\n\n

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Once uploaded, any virtual machine using the infrastructure can use the file as of right now.<\/p>\n\n\n\n

It is now time to create the VM. On the left-hand side, select “Virtual Machines”. You’ll be presented with a set of fields and options. First of all, as we are dealing here with a Windows-based VM, tick the box “Use VM Template:” and select the option “harvester-public\/windows-iso-image-base-template”. As stated by SUSE in their documention<\/a>, it is used to add a volume, a disk so, where all the necessary optimised drivers will be stored, so you can refer to that when installing the operating system. If you already used KVM for virtualization, you are on familiar ground. We are talking about paravirtualized VirtIO<\/a> drivers.<\/p>\n\n\n\n

We start to see some Kubernetes references: a namespace is required in order to group the corresponding Kubernetes objects. Reminder: The Kubernetes backend will be in charge of managing your VM. For that, a new set of CRDs (CustomResourceDefinition) were added to handle that.<\/p>\n\n\n\n

For instance, here is the definition of a VM: VirtualMachineInstance<\/a>.
We create a new namespace; let’s call it omrun. We also set the name, as well as a short description. Based on your application requirements, specify the CPU and memory resources.<\/p>\n\n\n\n

Here, I’m not going to implement a production ready instance of OMrun, so I won’t be greedy on that: 4 CPUs and 8GB of memory.<\/p>\n\n\n\n

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Move on to the storage. By selecting the “Volumes” item, we will be able to setup all disks and virtual CD-ROM. Since we use a VM template, we can see that Harvester already provisioned for us some storage devices:<\/p>\n\n\n\n