Name
Abstract | ||
|---|---|---|
In this paper, we propose a mechanism based on Self-Organizing Maps for analyzing the resource consumption behaviors and detecting possible anomalies in data centers for Network Function Virtualization (NFV). Our approach is based on a joint analysis of two historical data sets available through two separate monitoring systems: system-level metrics for the physical and virtual machines obtained from the monitoring infrastructure, and application-level metrics available from the individual virtualized network functions. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, highlight some of the capabilities of our system to identify interesting points in space and time of the evolution of the monitored infrastructure.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3341105.3374110 | SAC '20: The 35th ACM/SIGAPP Symposium on Applied Computing
Brno
Czech Republic
March, 2020 |
Keywords | DocType | ISBN |
Self-Organizing Maps, Machine Learning, Network Function Virtualization | Conference | 978-1-4503-6866-7 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giacomo Lanciano | 1 | 1 | 1.37 |
| Antonio Ritacco | 2 | 1 | 1.37 |
| Tommaso Cucinotta | 3 | 472 | 38.23 |
| Marco Vannucci | 4 | 94 | 15.60 |
| Antonino Artale | 5 | 1 | 0.69 |
| Luca Basili | 6 | 1 | 0.35 |
| Enrica Sposato | 7 | 1 | 0.69 |
| Joao Barata | 8 | 1 | 0.35 |