The OneLearn Lab on Demand platform provides you with several options when selecting the delivery fabric for your lab. The Lab on Demand platform supports multiple virtualization fabrics and cloud provider platforms, including Azure, Amazon, Hyper-V and VMware. There are options for interacting with platforms to which the Lab on Demand platform is not currently fully integrated. The Lab on Demand platform provides additional flexibility and choice as a result of its ability to integrate directly with cloud providers to provision and manage environments that do not leverage virtual machines. The graphic below represents the fabrics to which the Lab on Demand platform is partially or fully integrated.

Virtual Machine Hosting Fabrics

Virtualization has proven to be one of the most reliable training methods by ensuring all students receive the same starting environment. With the Lab on Demand platform, you have four virtualization hosting platforms options: Hyper-V, VMware, Amazon Web Services, and Microsoft Azure. Before you can begin to compare virtual machine hosting fabrics, you need to understand the different options available for virtual machines on the Lab on Demand platform.

  • Hosted in Local Datacenters: Hosting your virtual machine in the Lab on Demand datacenters will result in lower financial costs compared the cloud-hosted alternative and more control over virtual machine design. In addition, if you are in or near a region covered by the Lab on Demand datacenters (North America, Europe, APAC), this option will likely result in low latency connections.
  • Hosted on a Cloud Platform: Hosting your virtual machine in the cloud will result in higher financial costs. In exchange, the cloud platforms offer worldwide datacenter locations and may provide better connections if you typically experience poor connections to Lab on Demand data center locations.
  • Nested Virtualization: This option provides the ability to run another hypervisor within your virtual machine.
  • Linux Support: Linux support is provided for virtual machines running the three major Linux distributions and their variants: Debian 8+ (including Ubuntu and Kali), SUSE 11+ (including openSUSE), and Red Hat Enterprise Linux 6.4+ (including CentOS).
  • Automatic Screen Scaling: This Lab on Demand client feature scales the virtual machine desktop to match the view window.
  • Easily Migrate Images: Virtual machine images can be migrated easily, with no real limitations, out of one fabric and then sent to another location on the same fabric—for example, from your local network or cloud provider to the Lab on Demand datacenters.
  • Move Individual Disks: Individual virtual disks can be moved easily, with no real limitations, from one file location to another on the same or different fabric—for example, from an “old_vhds” folder to a “new_vhds” folder.
  • Start States: Start states permit a lab to be launched in an already running state, as compared to the standard format in which the virtual machines must go through a startup process. This enables faster lab launch times.
  • Highly Configurable Networking: Fabrics that use highly configurable networking permit complex networking configurations between virtual machines and the internet.
  • Advanced Network Interfacing/Monitoring: When configuring a virtual machine in a fabric that uses advanced network interfacing and monitoring, you can select from one of many network adapter types, and then configure advanced network monitoring on the adapter.
  • GPU/High Compute Options: Fabrics that require dedicated GPUs for high-performance or 3D graphics rendering can run on specialized hardware for compute-intensive scenarios.
  • High User Concurrency: Fabrics that support high user concurrency on the Lab on Demand platform can support 5000+ users simultaneously launching from a single location.

No single fabric can use all of these options, so you need to determine the ones that will best meet your needs. While reviewing the previous list, you should have identified a few key options that stand out as components you would like to use. Before you can decide on the fabric you want to use, you need to understand which options are available for each hosting fabric on the Lab on Demand platform. The table below will help you with your decision.

* AWS currently supports bare metal OS installations in public preview—allowing you to install your own hypervisor—however, this is an entire server stack and is extremely costly.

** Hyper-V on the Lab on Demand platform supports automated screen resolution adjustment with the installation of a helper app. True screen scaling is only available when using enhanced session mode.

*** VMware on the Lab on Demand platform does not support the addition of true GPUs—like the cloud providers do—or high compute options. Instead, it offers a discrete GPU, which permits options like 3D rendering.

Non-Virtualized Training Fabrics

Virtualization has become the technical training standard for creating uniform learning experiences for multiple students. While virtualization remains a necessity when you need to mirror an on-premises environment, if you do not need this type of environment, the Lab on Demand platform offers options for training in non-virtualized environments. A non-virtualized environment eliminates the need to maintain virtual machine images and removes the impact of latency from a remote connection on the learning experience. Non-virtualized training options include full integration with cloud providers via Lab on Demand’s Cloud Slice technology, side-by-side training with direct access to web and local applications, and the ability to enhance both of these options by adding your own integrations using Life Cycle Actions. Let’s take a look at the Lab on Demand methods for facilitating non-virtualized training, starting with the Cloud Slice technology and the cloud providers with which the Lab on Demand platform is already integrated.

Currently, the Lab on Demand platform features full integration with Microsoft Azure and Amazon Web Services (AWS). As outlined below, although the same overarching categories of capabilities apply to cloud slices of both providers, the way in which those capabilities are facilitated works a bit differently for each provider.If your training uses either Azure or AWS, you should use Cloud Slice. Cloud Slice can even be used in combination with virtualized hosting if your training scenario involves a hybrid environment. You can enhance the Cloud Slice experience even further by using Life Cycle Actions. However, if your training does not use one of these providers, you will need to use the direct access method. 

Direct Access

With the Lab on Demand platform, you can configure student labs to launch directly into any web-based portal while keeping the instructions visible. This permits fluid training of a web application in which students are presented with interactive instructions while maintaining full interaction with the web portal, without needing to switch back and forth between programs such as a document and a web browser. Beyond web-based applications, a lab that uses this format can also be tailored to a program on a user’s local computer. Instead of maintaining a virtual machine to teach Windows PowerShell, for example, you could have students launch PowerShell on their local computer, and it will run side-by-side with their instructions to permit a learning experience in an environment they use regularly. Lab Instructions can even be designed to Interact directly with desktop apps that support URL activation, such as Visual Studio Code.

Direct Access is ideal for any training that does not need the backbone of a virtualized fabric and is oriented around a platform that is not currently integrated with the Lab on Demand platform. However, to bring your training experience to the next level, you should include Life Cycle Actions to integrate with outside platforms.

Life Cycle Actions

While the Lab on Demand platform’s Cloud Slice technology permits automated integration with the APIs of certain cloud providers, the Life Cycle Actions feature can be used to expand on these integrations—for example, initiating Azure PowerShell commands in addition to deploying ARM templates by using a Cloud Slice—or to make your own integrations with other platforms. This could be your own proprietary platform or any platform that has a publicly accessible API. At the beginning and end of a lab session, you can manipulate that API to automatically create and remove resources that are used for training. 

Life Cycle Actions can provide a smoother training experience for the student by removing the actions that are not a part of the learning goals.

Best Practices

  • Don’t be afraid of change.
    • If you have worked on Hyper-V for a long time and realize that VMware has a feature you need, give it a shot and see how it works for you.
  • Create your virtual machines natively on the Lab on Demand platform instead of creating them locally, and then copying them to the Lab on Demand platform.
    • This reduces the number of files transferred and reduces the time needed to create and update labs.
  • Don’t get “stuck” in a virtualized world.
    • When virtualization is not necessary, don’t rely on a virtual machine simply out of habit. Embrace cloud training fabrics.
    • Harness cloud features if possible–for example, use Azure Cloud Shell instead of a local SSH client.
  • Reuse content when possible.
    • If you have a single virtual machine that can be used in numerous labs, it can cut down on your lab development time.

From our standpoint

Virtualization in hands-on learning will continue to be a part of the training landscape, as on-premises and hybrid deployments still exist. However, as the cloud options grow, and the use of the cloud further encompasses the workplace, consider harnessing direct cloud training as much as possible. Ensure that you know the goals and intentions of your lab ahead of time so that you can choose the best fabric from the start.

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