Name
Title | ||
|---|---|---|
Proposal and Investigation of a Convolutional and LSTM Neural Network for the Cost-Aware Resource Prediction in Softwarized Networks |
Abstract | ||
|---|---|---|
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%. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/fi13120316 | FUTURE INTERNET |
Keywords | DocType | Volume |
Network Function Virtualization, computing resources, machine learning, long short term memory, convolutional network | Journal | 13 |
Issue | Citations | PageRank |
12 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vincenzo Eramo | 1 | 0 | 0.34 |
| Francesco Valente | 2 | 0 | 0.34 |
| Tiziana Catena | 3 | 1 | 1.76 |
| Francesco Giacinto Lavacca | 4 | 61 | 4.89 |