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%.

Proposal and investigation of a convolutional and lstm neural network for the cost-aware resource prediction in softwarized networks† / Eramo, V.; Valente, F.; Catena, T.; Lavacca, F. G.. - In: FUTURE INTERNET. - ISSN 1999-5903. - 13:12(2021), pp. 1-16. [10.3390/fi13120316]

Proposal and investigation of a convolutional and lstm neural network for the cost-aware resource prediction in softwarized networks†

Eramo V.
Primo
;
Valente F.;Catena T.;Lavacca F. G.
2021

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%.
2021
Computing resources; convolutional network; long short term memory; machine learning; network function virtualization
01 Pubblicazione su rivista::01a Articolo in rivista
Proposal and investigation of a convolutional and lstm neural network for the cost-aware resource prediction in softwarized networks† / Eramo, V.; Valente, F.; Catena, T.; Lavacca, F. G.. - In: FUTURE INTERNET. - ISSN 1999-5903. - 13:12(2021), pp. 1-16. [10.3390/fi13120316]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1599940
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