Forecasting operation metrics for virtualized network functions

Brau F.;
2021-01-01

Abstract

Network Function Virtualization (NFV) is the key technology that allows modern network operators to provide flexible and efficient services, by leveraging on general-purpose private cloud infrastructures. In this work, we investigate the performance of a number of metric forecasting techniques based on machine learning and artificial intelligence, and provide insights on how they can support the decisions of NFV operation teams. Our analysis focuses on both infrastructure-level and service-level metrics. The former can be fetched directly from the monitoring system of an NFV infrastructure, whereas the latter are typically provided by the monitoring components of the individual virtualized network functions. Our selected forecasting techniques are experimentally evaluated using real-life data, exported from a production environment deployed within some Vodafone NFV data centers. The results show what the compared techniques can achieve in terms of the forecasting accuracy and computational cost required to train them on production data.
2021
Inglese
Proceedings - 21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2021
Institute of Electrical and Electronics Engineers Inc.
10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA
9
596
605
10
21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2021
Esperti anonimi
2021
aus
scientifica
Forecasting
LSTM
NFV
Operations
Time-series
Goal 9: Industry, Innovation, and Infrastructure
4 Contributo in Atti di Convegno (Proceeding)::4.1 Contributo in Atti di convegno
Cucinotta, T.; Lanciano, G.; Ritacco, A.; Brau, F.; Galli, F.; Iannino, V.; Vannucci, M.; Artale, A.; Barata, J.; Sposato, E.
273
10
4.1 Contributo in Atti di convegno
none
info:eu-repo/semantics/conferencePaper
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