Authors:
Tommaso Cucinotta
1
;
Giacomo Lanciano
2
;
Antonio Ritacco
1
;
Marco Vannucci
1
;
Antonino Artale
3
;
Joao Barata
4
;
Enrica Sposato
3
and
Luca Basili
3
Affiliations:
1
Scuola Superiore Sant’Anna, Pisa, Italy
;
2
Scuola Superiore Sant’Anna, Pisa, Italy, Scuola Normale Superiore, Pisa, Italy
;
3
Vodafone, Milan, Italy
;
4
Vodafone, Lisbon, Portugal
Keyword(s):
Self-organizing Maps, Machine Learning, Network Function Virtualization.
Abstract:
In this paper, we tackle the problem of detecting anomalous behaviors in a virtualized infrastructure for network function virtualization, proposing to use self-organizing maps for analyzing historical data available through a data center. We propose a joint analysis of system-level metrics, mostly related to resource consumption patterns of the hosted virtual machines, as available through the virtualized infrastructure monitoring system, and the application-level metrics published by individual virtualized network functions through their own monitoring subsystems. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, show that our technique is able to identify specific points in space and time of the recent evolution of the monitored infrastructure that are worth to be investigated by a human operator in order to keep the system running under expected conditions.