Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Tiziana Catena"'
Publikováno v:
IEEE Access, Vol 8, Pp 200834-200850 (2020)
The high time required for the deployment of cloud resources in Network Function Virtualization network architectures has led to the proposal and investigation of algorithms for predicting traffic or the necessary processing and memory resources. How
Externí odkaz:
https://doaj.org/article/5f55e08c70b143048eb49a0047703c6d
Publikováno v:
Future Internet, Vol 13, Iss 12, p 316 (2021)
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
Externí odkaz:
https://doaj.org/article/568bac2fc8224a87ab54bd15f0d9f44f
Publikováno v:
Future Internet, Vol 12, Iss 11, p 196 (2020)
The high time needed to reconfigure cloud resources in Network Function Virtualization network environments has led to the proposal of solutions in which a prediction based-resource allocation is performed. All of them are based on traffic or needed
Externí odkaz:
https://doaj.org/article/87b3dc7f244b47cb97e4eae54f443bcc
Publikováno v:
Future Internet, Vol 11, Iss 3, p 71 (2019)
Network Function Virtualization is a new technology allowing for a elastic cloud and bandwidth resource allocation. The technology requires an orchestrator whose role is the service and resource orchestration. It receives service requests, each one c
Externí odkaz:
https://doaj.org/article/b9cf4d6c40e448caa2dd5d6e2919929c
Autor:
Vincenzo Eramo, Tiziana Catena
Publikováno v:
IEEE Transactions on Network and Service Management. 19:2929-2943
Publikováno v:
2022 IEEE 8th International Conference on Network Softwarization (NetSoft).
Publikováno v:
Computer Networks. 213:109111
Publikováno v:
ICTON
The paper investigates resource allocation problems in Network Function Virtualization (NFV) network architectures in which the datacenters are interconnected by an Elastic Optical Network and the offered traffic is predicted by a Seasonal Autoregres