Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CCGrid51090.2021.00069 | 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid) |
Keywords | DocType | ISBN |
Forecasting,NFV,Operations,Time-series,LSTM | Conference | 978-1-7281-9587-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tommaso Cucinotta | 1 | 472 | 38.23 |
| Giacomo Lanciano | 2 | 1 | 1.37 |
| Antonio Ritacco | 3 | 1 | 1.37 |
| Fabio Brau | 4 | 0 | 0.34 |
| Filippo Galli | 5 | 0 | 0.34 |
| Vincenzo Iannino | 6 | 1 | 1.42 |
| Marco Vannucci | 7 | 94 | 15.60 |
| Antonino Artale | 8 | 1 | 0.69 |
| João Barata | 9 | 0 | 0.34 |
| Enrica Sposato | 10 | 1 | 0.69 |