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EnglishMeeting 5G high bandwidth rates, ultra-low latencies, and high reliabilities requires of network infrastructures that automatically increase/decrease the resources based on their customers' demand. An autonomous and dynamic management of a 5G network infrastructure represents a challenge, as any solution must account for the radio access network, data plane traffic, wavelength allocation, network slicing, and network functions' orchestration. Furthermore, federation among administrative domains (ADs) must be considered in the network management. Given the increased dynamicity of 5G networks, artificial intelligence/machine learning (AI/ML) solutions are strong candidates able to learn, and take quick provisioning decisions upon fast changes in network conditions. Therefore, this chapter presents an analysis of the state-of-the-art solutions for 5G networks' management, where AI/ML solutions are discussed and compared with traditional methods. Additionally, the chapter provides a technology overview of both standards, and existing solutions regarding the 5G network management, and directions toward the integration of AI/ML in 5G networks.
Self-Managed 5G Networks / Martin-Perez, Jorge; Antevski, Kiril; Guimaraes, Carlos; Bernardos, C. J.; Papagianni, Chrysa; de Vleeschauwe, Danny; Magoula, Lina; Barmpounakis, Sokratis; Kontopoulos, Panagiotis; Koursioumpas, Nikolaos; Sgambelluri, Andrea; Paolucci, Francesco; Valcarenghi, Luca; Garcia-Saavedra, Andres; Li, Xi; Puligheddu, Corrado; Chiasserini, Carla Fabiana; Casetti, Claudio Ettore; Mangues-Bafalluy, J.; Martínez, J. Baranda R.; Zeydan, Engin. - STAMPA. - (2021), pp. 69-100. [10.1002/9781119675525.ch4]
| Titolo: | Self-Managed 5G Networks | |
| Autori: | ||
| Data di pubblicazione: | 2021 | |
| Titolo del libro: | Communications Network and Service Management In the Era of Artificial Intelligence and Machine Learning | |
| Abstract: | Meeting 5G high bandwidth rates, ultra-low latencies, and high reliabilities requires of network ...infrastructures that automatically increase/decrease the resources based on their customers' demand. An autonomous and dynamic management of a 5G network infrastructure represents a challenge, as any solution must account for the radio access network, data plane traffic, wavelength allocation, network slicing, and network functions' orchestration. Furthermore, federation among administrative domains (ADs) must be considered in the network management. Given the increased dynamicity of 5G networks, artificial intelligence/machine learning (AI/ML) solutions are strong candidates able to learn, and take quick provisioning decisions upon fast changes in network conditions. Therefore, this chapter presents an analysis of the state-of-the-art solutions for 5G networks' management, where AI/ML solutions are discussed and compared with traditional methods. Additionally, the chapter provides a technology overview of both standards, and existing solutions regarding the 5G network management, and directions toward the integration of AI/ML in 5G networks. | |
| ISBN: | 9781119675501 | |
| Appare nelle tipologie: | 2.1 Contributo in volume (Capitolo o Saggio) |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| IEEE_BookChapter_VNF_Placement_SoTA_.pdf | Articolo principale | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Embargo: 03/09/2022 Richiedi una copia |
| Chiasserini-Self-managed.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2863452
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