Detection of Erratic Behavior in Load Balanced Clusters of Servers Using a Machine Learning Based Method
Abstract
With the explosion of the number of distributed applications, a new dynamic server environment emerged grouping servers into clusters, whose 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 detecting outliers indicating a possible problematic situation.
The method inspects the performance of the rest of the 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.