Using Autonomous Monitoring with Litmus Chaos Engine on Kubernetes

A few months ago, our friends at Maya Data joined our private Beta to give our Autonomous Log Monitoring platform a test run. During the test, they used their newly created Litmus Chaos Engine to generate issues in their Kubernetes cluster, and we managed to detect all of them successfully using our Machine Learning completely unsupervised. Needless to say, they were impressed!

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The Future of Monitoring is Autonomous

Monitoring today is extremely human driven. The only thing we’ve automated with monitoring to date is the ability to watch for metrics and events that send us alerts when something goes wrong. Everything else: deploying collectors, building parsing rules, configuring dashboards and alerts, and troubleshooting and resolving incidents, requires a lot of manual effort from expert operators that intuitively know and understand the system being monitored.

TL;DR

Monitoring today puts far too much burden on DevOps - requiring too much time and skill to build and maintain complex dashboards and fragile alert rules. Fortunately unassisted machine learning can be used to autonomously detect and find the root cause of critical incidents. Please read about it below, or try our free Autonomous Monitoring Platform now and start auto-detecting incidents in less than 5 minutes.

 

The Longer Version

 

Monitoring today is extremely human driven. The only thing we’ve automated with monitoring to date is the ability to watch for metrics and events that send us alerts when something goes wrong. Everything else: deploying collectors, building parsing rules, configuring dashboards and alerts, and troubleshooting and resolving incidents, requires a lot of manual effort from expert operators that intuitively know and understand the system being monitored.

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Part 1 - Machine learning for logs

In our last blog we discussed the need for Autonomous Monitoring solutions covering the three pillars of observability (metrics, traces and logs). At Zebrium we have started with logs (but stay tuned for more). This is because logs generally represent the most comprehensive source of truth during incidents, and are widely used to search for the root cause.

In our last blog we discussed the need for Autonomous Monitoring solutions to help developers and operations users keep increasingly large and complex distributed applications up and running.

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