The road towards self-healing systems, step two

You reach the highest possible level of monitoring and maintaining IT systems, when these systems regulate themselves. But the road to self-healing systems isn’t always an easy one. In our previous blog post, we described the first step towards self-healing: from monitoring to observability. In this edition, we’ll talk about self-healing systems, step two: from observability to self-learning.

As they depend on mission-critical IT systems, organizations do everything they can to prevent their systems from failing. Traditionally, you put monitoring systems in place to track the environment’s vital parameters and link actions to the values that you measure. We all know the classic example of a system that automatically adds extra storage, as soon as a storage usage threshold is exceeded.

In our previous blog post, we explained that these monitoring systems consider the environment they guard as a black box. Although they do take action, they don’t really ‘know’ why. That’s when we introduced the term ‘observability’. The evolution from traditional monitoring to observability requires a closer look on the log files of the system’s life signs. ‘Observing’ the situations that caused an alert allows for better, more precise action.

Self-healing systems, step two: from observability to self-learning

Now, we dispose of much more information about the platform we are watching. On top of that, quite some of that information has already been processed or prepared for us to interpret it more easily. Nonetheless in a state of observability, the system engineer is still the one to decide what to do, take action, review and learn from his mistakes. Adding machine learning leads to a self-learning system, an crucial step in the journey towards a truly self-healing system.

In short, a self-learning system is an adaptive system whose operation algorithm is worked out and improved by a learning process that is based on trial and error. As the system makes trial changes in the algorithm, it simultaneously monitors the results of these changes. This way, the system learns how to correctly interpret situations – and the actions they require.

Highly dynamic environments

All in all, the use of automation and self-learning systems isn’t really a matter of choice. It’s an inevitability. And as we all know, modern IT environments are more dynamic than ever. Instances come and go in a matter of hours or minutes. Microservices come into play in a matter of seconds. In a serverless environment, they add and delete new elements in just a few milliseconds – 24 hours a day, 7 days a week.

Keeping an eye on such a dynamic IT environment just isn’t humanly possible. Self-learning monitoring systems are capable of going through massive amounts of data in the blink of an eye. In other words, they pick up anything that is out of the ordinary. They reference the exceptions against earlier decisions, which leads to automated action. It is clear that traditional monitoring systems – sending out alerts via email or text – are far too slow for modern, lively IT environments.

A self-learning system builds the knowledge and experience to take the next step: from self-learning to self-healing.

Learn more about self-healing systems in our next blog!

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