Today's hyper-connectivity is pushing computing away from the Cloud, closer to connected devices, creating a new paradigm called Edge Computing. When solving problems related to response time, bandwidth, and data sovereignty, Edge Computing considerably increases software development and deployment complexity.

The Edge is a distributed infrastructure that expands from proximity data centers to connected devices. This means that it's not a question of managing a few regions anymore as Edge data centers range in the tens of thousands just in the US as of 2020. Even if this part is automated somehow using traditional automation tools, regions would need to be known in advance. Not to say, it will be very expensive to pre-allocate capacity in every proximity data center. Autoscaling capabilities of Cloud-related solutions will not cut it as they only scale locally.

Over a decade ago, System Admins were in great demand for their skills in maintaining reliable operation of computer systems for running software applications. As software life-cycle shortens, manually handling the systems and networking layers needed to run it, is not an option anymore. DevOps automates those tasks so companies could focus on building their application or business logic. According to DORA (DevOps Research and Assessment) a product line valued at about $100M can cost upto $5.6M to automate initially, but will deliver an ROI of about 12.5 times the investment.

Automation is great, but the “Dev” part of “DevOps” hints to “lots of custom code”; code that needs considerable changes every time there’s a new requirement on the software side. One of the biggest struggles of DevOps today is handling multi-region and multi-cloud use cases. Which raises the question: “If DevOps struggles to scale to few regions, how would it handle the scale of the Edge?”

When addressing scalability, automation is a key solution. However there are many levels of automation. Thanks to DevOps we know that a certain level of automation is possible and holds great benefits. The next level would be to automate the “Dev” in “DevOps” we referred to earlier; we call this level of automation “NoOps”.

A computing platform that has a “NoOps” automation level can scale to any number of data centers and devices. This is possible because any software or its dependencies is an ephemeral (Tau) unit (Byte) the platform can run, provision, replicate, and migrate dynamically. We call this type of computing platform: “Smart Computing”

At Taubyte, we’ve built this next generation computing platform! With a self-operating platform that extends from data centers to the far Edge, where connected devices, like IoT Gateways, are prevalent, we enable the true potential of the global computing infrastructure catalyzed by 5G and IoT technologies.

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