Speaker
Description
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.
