Proposal and Investigation of a Convolutional and LSTM Neural Network for the Cost-Aware Resource Prediction in Softwarized Networks

Vincenzo Eramo, Francesco Valente, Tiziana Catena and Francesco Giacinto Lavacca
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Vincenzo Eramo: Department of Information Engineering, Electronic, Telecommunication, “Sapienza” University of Rome, Via Eudossiana 18, 00184 Rome, Italy
Francesco Valente: Department of Information Engineering, Electronic, Telecommunication, “Sapienza” University of Rome, Via Eudossiana 18, 00184 Rome, Italy
Tiziana Catena: Department of Information Engineering, Electronic, Telecommunication, “Sapienza” University of Rome, Via Eudossiana 18, 00184 Rome, Italy
Francesco Giacinto Lavacca: Department of Information Engineering, Electronic, Telecommunication, “Sapienza” University of Rome, Via Eudossiana 18, 00184 Rome, Italy

Future Internet, 2021, vol. 13, issue 12, 1-16

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%.

Keywords: Network Function Virtualization; computing resources; machine learning; long short term memory; convolutional network (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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