Predictive AutoScaling with Artificial Intelligence

 If you are running Docker instances, Kubernetes, migrating to AWS, GCP or whatever big data critical movement, you know you have a problem, a performance problem.

Today's engeneers have what they only dreamt about in the past, the possibility to not have to improve the code, at critical moments, and be able to devise a good strategy with time, and a good plan, to make sure that the budgetary CTO's vigilance procedures don't encounter objections to growth.

To improve the decision making of when to scale on resources, it that machine learning can help us predict, the moment of high demand, and scale up, or save resources when demand is low.

Incoming API request, system miss-usage, intensive reporting queries and customer demands all compete for our DB's, Servers' and data transfer resources.


Predictive Autoscaling Medium Article

So for the companies that were at the past decade testablishing themselves as resource risk tranference incumbents, the competition will now become the one that makes the best CPU per dollar usage.

Using Machine Learning for Black Box Autoscaling Paper

What's the next frontier?

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