Join us November 13-15, 2018 in

Berlin, Germany

The next Open Infrastructure Summit is going to be in Denver, April 29-May 1. Check out the agenda.

Event Details

Please note: All times listed below are in Central Time Zone

Reduce your log noise using machine learning

Continous integration jobs can generate massive volumes of data. When a job fails, figuring out what went wrong can be a tedious process—one that involves investigating logs and discovering the root cause, often found in a fraction of the total job output.  By building a machine learning model from previous successful job runs, we can use the tools from the log-classify project to automatically extract anomalies from the logs of a particular run.

Similarly this principle can be applied to other use-cases as well like for example extracting anomalies from journald or other system wide regular log files. In this presentation additional use cases are covered and being discussed.

What can I expect to learn?

Attendees should expect to learn what the challenges of anomaly detection are in continuous integration jobs and how machine learning can provide a powerful service to assist failure investigations. The presentation will also show how the new Zuul Version 3 works and how it can be extended to integrate log proccessing models.

Wednesday, November 14, 1:40pm-2:20pm (12:40pm - 1:20pm UTC)
Difficulty Level: Intermediate
Red Hat
OpenStack Vulnerability Management Team (VMT) member working at Red Hat. FULL PROFILE
Dirk Mueller is a Senior Software Engineer working at SUSE currently focusing on Cloud, OpenStack, SUSE's deployment and OpenStack distribution named SUSE OpenStack Cloud. He's being developing for and using Linux for more than 15 years and is doing Software packaging, distribution and software development for more than 10 years.    FULL PROFILE