Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Antonio Cianfrani"'
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
Publikováno v:
Energies, Vol 9, Iss 6, p 470 (2016)
We present a model to evaluate the server lifetime in cloud data centers (DCs). In particular, when the server power level is decreased, the failure rate tends to be reduced as a consequence of the limited number of components powered on. However, th
Externí odkaz:
https://doaj.org/article/13e16d2ccd7a4115a96f050268aa93b4
Autor:
Marco Polverini, Antonio Cianfrani, Marco Listanti, Giulio Siano, Francesco Giacinto Lavacca, Carlo Candeloro Campanile
Publikováno v:
IEEE Transactions on Network and Service Management. 20:14-29
Autor:
Marco Polverini, Francesco G. Lavacca, Jaime Galan-Jimenez, Davide Aureli, Antonio Cianfrani, Marco Listanti
Publikováno v:
2022 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN).
Publikováno v:
2022 18th International Conference on Network and Service Management (CNSM).
Autor:
Marco Polverini, Antonio Cianfrani, Francesco Giacinto Lavacca, Jaime Galán-Jiménez, Vincenzo Eramo
Publikováno v:
IEEE Transactions on Network and Service Management. 18:1445-1460
Service Function Chaining (SFC) is an enabling technology to provide end-to-end service differentiation according to specific user requirements. Although emerging technologies such as Software-Defined Networking (SDN) and Network Function Virtualizat
Publikováno v:
NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium.
Publikováno v:
2022 25th Conference on Innovation in Clouds, Internet and Networks (ICIN).
Publikováno v:
IEEE Transactions on Network and Service Management. 17:1924-1940
Self-driving networks represent the next step of network management techniques in the close future. A fundamental point for such an evolution is the use of Machine Learning based solutions to extract information from data coming from network devices