The process of mainframe machines managing and administration requires not only specialized expert knowledge based on many years of experience but also on appropriate tools provided by a machine performance management system, e.g. the Resource Measurement Facility (RMF). The aim of this paper is to show some preliminary results of Z-RAYS system construction that is built basing on machine learning (ML) techniques. It allows automatic detection of anomalies and generation of early warnings about some errors that can appear in the mainframe to support mainframe management process. Presented results are based on extensive simulations that were done basing on the IBM emulator. We focus on determining the degree of the metrics variability, the degree of the data repeatability in metrics, some approaches in metrics anomaly detection and solutions for event correlation detection in metrics.
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