Eliminate Enterprise IT Oversights with Digital Twins

Having a testbed or lab for IT infrastructure testing purposes is a great asset that most enterprise IT shops have long come to embrace. However, due to the fact these setups often differ widely from production environments, the benefits are limited. While making an infrastructure change in a test environment may seemingly work as intended, it may not factor in several potential issues found when implementing these same changes while in production. This is where digital twins come into play.

Let’s look at the purpose of digital twins, how they work, and ways they will reduce infrastructure downtime caused by human error and oversights that continuously plague enterprise IT operations.

What Is a Digital Twin and How Do They Work?

Digital twins are virtual models that simulate real-world and complex physical entities such as enterprise IT infrastructures. Soon, digital twins of networks, systems, and endpoints can be created using real-time data pulled directly from production. These twins can then be used to test planned infrastructure adds/changes and validate various hardware/software updates before implementing them into an active infrastructure. Twins have the potential to significantly increase the likelihood of successful production changes while eliminating costly mistakes due to human error or incompatibilities. Infrastructure digital twins work as follows:

Mimics a production environment – Digital twins mimic virtual infrastructure components and data pulled from production in real-time. The software-defined twins help uncover unexpected oversights that may not have been considered.

Sandbox for no-risk performance change testing – A digital twin gives ITOps teams the freedom and flexibility to test complex changes or work out potential problems that lead to performance degradation without the fear of making the problem worse.

Automate changes directly from the digital twin – In some cases, it’s possible to push infrastructure configurations directly from the digital twin environment to the production environment after thorough testing is complete.

Prediction models – When seeking to scale or add new infrastructure components, a digital twin can be used to see how the proposed additions will act in a production environment. The resulting data can then be analyzed to determine aspects such as compute requirements and network traffic loads to ensure that the existing network and systems will be able to handle the new components and systems without causing undue harm to existing applications and services.

Ultimately, digital twins can reduce the time required to prepare for complex infrastructure adds/changes while allowing ITOps teams to eliminate human error and be certain the proposed changes will not negatively impact other aspects of the production environment. The result is an increase in operational efficiency with a far lower chance of error. Twins can also be used to test various infrastructure firmware/OS/application patches and upgrades that can be thoroughly vetted inside the twin environment to ensure the updates do not have any unforeseen side effects that could impact business operations.

Infrastructure Digital Twins Not Quite Ready – But Rapidly Evolving

In most cases, it’s still not possible to create an infrastructure digital twin that fully recreates mid- to large-sized infrastructures. The amount of CPU, memory, storage, and networking resources required to mimic a complete infrastructure in a virtual setting is simply too high.

What can be accomplished, however, is to recreate a portion of the network based on the proposed changes and goals you want to achieve. This requires a digital twin environment that is flexible enough to add/remove various infrastructure components to the point where any unforeseen challenges will be spotted and eliminated prior to placing those changes into production.

At this point, it still requires that twin administrators have a deep understanding of their existing environment and what components should be in place that will help to identify problems. In time, however, digital twins will evolve to the point where automation and artificial intelligence will do this on the administrators’ behalf. It’s at this point where the true power of the digital twin will emerge.

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