01581nas a2200241   4500008004100000020001800041245006800059210006700127260005900194520079900253653002101052653003601073653002501109100002201134700002101156700002301177700002001200700002101220700001701241700002001258700001701278856004401295       2020                        eng d  a978145036866700aSOM-Based Behavioral Analysis for Virtualized Network Functions0 aSOMBased Behavioral Analysis for Virtualized Network Functions  aNew York, NY, USAbAssociation for Computing Machinery3 aIn this paper, we propose a mechanism based on Self-Organizing Maps for analyzing the resource consumption behaviors and detecting possible anomalies in data centers for Network Function Virtualization (NFV). Our approach is based on a joint analysis of two historical data sets available through two separate monitoring systems: system-level metrics for the physical and virtual machines obtained from the monitoring infrastructure, and application-level metrics available from the individual virtualized network functions. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, highlight some of the capabilities of our system to identify interesting points in space and time of the evolution of the monitored infrastructure.10amachine learning10anetwork function virtualization10aself-organizing maps1 aLanciano, Giacomo1 aRitacco, Antonio1 aCucinotta, Tommaso1 aVannucci, Marco1 aArtale, Antonino1 aBasili, Luca1 aSposato, Enrica1 aBarata, Joao  uhttps://doi.org/10.1145/3341105.3374110