| Autor: |
Vincenzo Eramo, Francesco Valente, Tiziana Catena, Francesco Giacinto Lavacca |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Future Internet, Vol 13, Iss 12, p 316 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1999-5903 |
| DOI: |
10.3390/fi13120316 |
| Popis: |
Resource prediction algorithms have been recently proposed in Network Function Virtualization architectures. A prediction-based resource allocation is characterized by higher operation costs due to: (i) Resource underestimate that leads to quality of service degradation; (ii) used cloud resource over allocation when a resource overestimate occurs. To reduce such a cost, we propose a cost-aware prediction algorithm able to minimize the sum of the two cost components. The proposed prediction solution is based on a convolutional and Long Short Term Memory neural network to handle the spatial and temporal correlations of the need processing capacities. We compare in a real network and traffic scenario the proposed technique to a traditional one in which the aim is to exactly predict the needed processing capacity. We show how the proposed solution allows for cost advantages in the order of 20%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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