TY - CONF T1 - SOM-Based Behavioral Analysis for Virtualized Network Functions T2 - Proceedings of the 35th Annual ACM Symposium on Applied Computing Y1 - 2020 A1 - Giacomo Lanciano A1 - Antonio Ritacco A1 - Tommaso Cucinotta A1 - Marco Vannucci A1 - Antonino Artale A1 - Luca Basili A1 - Enrica Sposato A1 - Joao Barata KW - machine learning KW - network function virtualization KW - self-organizing maps AB - In 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. JF - Proceedings of the 35th Annual ACM Symposium on Applied Computing PB - Association for Computing Machinery CY - New York, NY, USA SN - 9781450368667 UR - https://doi.org/10.1145/3341105.3374110 ER -