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.

Behavioral Analysis for Virtualized Network Functions : A SOM-based Approach

Lanciano, Giacomo;
2020

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.
2020
Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni
10th International Conference on Cloud Computing and Services Science (CLOSER 2020)
Praga
7-9 maggio 2020
Proceedings of the 10th International Conference on Cloud Computing and Services Science (CLOSER 2020)
SCITEPRESS
978-989-758-424-4
Self-organizing Maps; Machine Learning; Network Function Virtualization
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/131264
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