Using Self-Organizing Maps for the Behavioral Analysis of Virtualized Network Functions
| Autor: | Giacomo Lanciano, Fabio Brau, Joao Barata, Tommaso Cucinotta, Enrica Sposato, Marco Vannucci, Antonio Ritacco, Antonino Artale |
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| Přispěvatelé: | D. Ferguson, C. Pahl, M. Helfert, Lanciano, Giacomo, Ritacco, Antonio, Brau, Fabio, Cucinotta, Tommaso, Vannucci, Marco, Artale, Antonino, Barata, Joao, Sposato, Enrica |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Self-organizing map
Network Functions Virtualization Self-organizing maps Network function virtualization business.industry Computer science Distributed computing Cloud computing Self-Organizing Map Space (commercial competition) computer.software_genre Article Machine Learning Behavioral analysis Virtual machine Order (exchange) Machine learning Data center Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni business computer Network Function Virtualization |
| Zdroj: | Cloud Computing and Services Science Communications in Computer and Information Science ISBN: 9783030723682 CLOSER (Selected Papers) |
| Popis: | Detecting anomalous behaviors in a network function virtualization infrastructure is of the utmost importance for network operators. In this paper, we propose a technique, based on Self-Organizing Maps, to address such problem by leveraging on the massive amount of historical system data that is typically available in these infrastructures. Indeed, our method consists of a joint analysis of system-level metrics, provided by the virtualized infrastructure monitoring system and referring to resource consumption patterns of the physical hosts and the virtual machines (or containers) that run on top of them, and application-level metrics, provided by the individual virtualized network functions monitoring subsystems and related to the performance levels of the individual applications. The implementation of our approach has been validated on real data coming from a subset of the Vodafone infrastructure for network function virtualization, where it is currently employed to support the decisions of data center operators. Experimental results show that our technique is capable of identifying specific points in space (i.e., components of the infrastructure) and time of the recent evolution of the monitored infrastructure that are worth to be investigated by human operators in order to keep the system running under expected conditions. |
| Databáze: | OpenAIRE |
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