Naslov
CARNET OAV Architecture Analysis

Autori
Regvart, Damir ; Filipovska, Sonja ; Cunningham, Donal ; Golub, Ivana ; Gandia Carriedo, Maria Isabel ; Naegele-Jackson, Susanne

Kolaboracija
GÉANT Association

Vrsta, podvrsta
Ostale vrste radova, ostalo

Godina
2021

Ključne riječi
Open Digital Architecture ; OAV ; Transforming Services with Orchestration and Automation ; e ODA reference model

Sažetak
Abstract The document analyses the mapping of the CARNET architecture to the TM Forum’s Open Digital Architecture aiming to provide a standardised view of the components and implementations of orchestration, automation and virtualisation within the NRENs. Analysing NREN architectures from an orchestration, automation and virtualisation (OAV) point of view using a common reference architecture helps align efforts, and find similarities in the way different functionalities and components are implemented, which in turn facilitates potential collaboration between organisations and future interoperability. In pursuit of this goal, the GN4-3 Network Technologies and Services Development Work Package (WP6), Network Services Evolution & Development task (T2) selected the TM Forum Open Digital Architecture (ODA) as a reference blueprint architecture that can be used for such cross-comparison. The rationale for that choice is described in Deliverable D6.6 Transforming Services with Orchestration and Automation [D6.6]. The WP6T2 team is working with NRENs to perform such mappings. In this document the team reports on an analysis of the different functional aspects of the CARNET network management system architecture, and how their components map to the ODA reference model.The mapping highlights the main characteristics and capabilities of the current CARNET network architecture, and how they fit into the main functional domains of ODA. The analysis was carried out by the network architects supported by the WP6T2 team.

Izvorni jezik
Engleski

Napomena
© GÉANT Association on behalf of the GN4-3
project.
The research leading to these results has received
funding from the European Union’s Horizon 2020
research and
innovation programme under Grant Agreement No.
856726 (GN4-3)

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