Name
Abstract | ||
|---|---|---|
With the increasing tendency on data rates in forthcoming communication networks, availability is a crucial aspect to guarantee Quality of Service (QoS) requirements. The possibility of predicting the lifetime of networking hardware can be a key to improve the overall network QoS. This paper proposes a generic Machine Learning (ML) based framework that learns how to mimic the mathematical model behind the lifetime of network line cards. Results show that a good precision (85%) and recall (close to 100%) on the estimation can be achieved regardless the type of line cards the network is composed of. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/NOMS47738.2020.9110455 | NOMS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Luis Herrera | 1 | 0 | 0.34 |
| Marco Polverini | 2 | 187 | 16.25 |
| Jaime Galán-Jiménez | 3 | 28 | 11.00 |