9-13 July 2018
Sofia, Bulgaria
Europe/Sofia timezone

Detection of erratic behavior in load balanced clusters of servers using a machine learning based method

10 Jul 2018, 15:30
Hall 10 (National Palace of Culture)

Hall 10

National Palace of Culture

presentation Track 8 – Networks and facilities T8 - Networks and facilities


Martin Adam (Acad. of Sciences of the Czech Rep. (CZ))


With the explosion of the number of distributed applications, a new dynamic server environment emerged grouping servers into clusters, which utilization depends on the current demand for the application.

To provide reliable and smooth services it is crucial to detect and fix possible erratic behavior of individual servers in these clusters. Use of standard techniques for this purpose delivers suboptimal results.

We have developed a method based on machine learning techniques which allows to detect outliers indicating a possible problematic situation. The method inspects the performance of the rest of a cluster and provides system operators with additional information which allows them to identify quickly the failing nodes. We applied this method to develop a Spark application using the CERN MONIT architecture and with this application we analyzed monitoring data from multiple clusters of dedicated servers in the CERN data center.

In this contribution we present our results achieved with this new method and with the Spark application for analytics of CERN monitoring data.

Primary authors

Martin Adam (Acad. of Sciences of the Czech Rep. (CZ)) Luca Magnoni (CERN)


Dagmar Adamova (Acad. of Sciences of the Czech Rep. (CZ)) Mr Martin Pilát (Charles University in Prague)

Presentation Materials