GÉANT Project white papers

GN5-1 white papers


The GÉANT p​roject has a range of white papers associated with the activities. These will be made available on this page once published.​​​​​​​​

OAV Terminology

OAV Terminology Published date | 29 October 2024

This document provides a list of terms and abbreviations in the context of Orchestration, Automation and Virtualisation. Definitions are provided based on standardisation documents wherever possible; some have also been extended to reflect the understanding of the terms as used by a large number of NRENs in the GÉANT community. As of version 1.1, the document has also been adopted by the GNA-G Network Automation working group as their reference terminology.

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Fibre Sensing Focus Group: Conclusions and Future Work

Fibre Sensing Focus Group: Conclusions and Future Work

Published date | 13 May 2024

In January 2024 a new Network Development incubator focus group was formed with a goal to explore the need for and interest in fibre sensing technologies among National Research and Education Networks involved in the GN5-1 project and their applicability, usage and usefulness for research and education networks. This document summarises the findings of the focus group and outlines possible future work.

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Software Licence Selection and Management in GÉANT

Software Licence Selection and Management in GÉANT

Published date | 5 March 2024

This document provides a detailed guide to software licence selection, declaration, compliance and management in GÉANT. Intended for software development teams, it contains both essential information and practical step-by-step instructions. It is divided into two main parts: Essential Aspects and Elements of Software Licensing, and Complying with a Selected Licence.

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GÉANT Core Time/Frequency Network (GÉANT C-TFN) Network Development Incubator Report (Public)

GÉANT Core Time/Frequency Network (GÉANT C-TFN) Network Development Incubator Report (Public)

Published date | 21 February 2024

Following an 8-month time/frequency (T/F) incubator project, this report sets out recommendations for building a GÉANT Core Time/Frequency Network (C-TFN) comprising federated cross-border T/F links that interconnect existing national T/F networks. The report includes the proposed architecture of the GÉANT C-TFN, topology options, cost modelling, sustainability, funding, operation and maintenance, recommendations for GN5-2, and areas requiring further investigation.

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Status of Cyber Security Awareness within National Research and Education Networks

Status of Cyber Security Awareness within National Research and Education Networks

Published date | 1 February 2024

This report describes the outcomes of a survey conducted by GN5-1 Work Package 8, Task 2 Human Factor (WP8 T2), subtask Awareness, on the status of cyber security awareness within the National Research and Education Networks.

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QKD Concepts and Considerations

QKD Concepts and Considerations

Published date | 31 January 2024

This document provides National Research and Education Networks (NRENs) in the GÉANT community with an overview of current issues related to Quantum Key Distribution (QKD), describing state-of-the-art solutions and ongoing technological challenges.

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PMP OAV Architecture Analysis

PMP OAV Architecture Analysis

Published date | 21 December 2023

The Performance Measurement Platform (PMP) is a distributed measurement platform consisting of small nodes with pre-installed perfSONAR software. It performs regular measurements towards selected measurement points and includes central components such as a Measurement Archive and a Dashboard. This document analyses the mapping of the PMP service architecture to the TM Forum’s Open Digital Architecture to provide a standardised view of the orchestration, automation and virtualisation components and implementations within the service.

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Passkeys Use and Deployment for R&E Services

Passkeys Use and Deployment for R&E Services

Published date | 27 November 2023

Passkeys are a more secure and convenient alternative to passwords, which they aim to replace as the primary method for account authentication. This white paper describes how they work; their usage; key aspects of their implementation, in particular for R&E services; and next steps with regard to development, transition and adoption.

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OAV Maturity Model

OAV Maturity Model

Published date | 7 November 2023

The Orchestration, Automation and Virtualisation (OAV) maturity model presented in this document has been created by the Network Development Work Package (WP6) of the GN5-1 project to provide a tool to compare and recognise similar goals and needs across NRENs, thereby boosting collaboration and cooperation in OAV in the research and education space. The maturity model enables organisations to assess their OAV capabilities, described through a set of dimensions. The results of the maturity model survey can help organisations evaluate their current state and determine their objectives in terms of OAV evolution, as well as establish a roadmap to follow in order to increase their OAV maturity level.

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Argus OAV Architecture Analysis

Argus OAV Architecture Analysis

Published date | 30 June 2023

Argus is an open-source tool for Network Operation Centres and service desks to aggregate incidents from all their monitoring applications into a single, unified dashboard and notification system. This document analyses the mapping of the Argus 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 Argus deployment at Sikt, the Norwegian NREN.

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PMP Data Analysis

PMP Data Analysis

Published date | 6 April 2023

The Performance Measurement Platform (PMP) consists of a number of small nodes that undertake regular measurements towards a few Measurement Points (MPs) located in the core of the GÉANT network. This white paper presents an overview of the work and results of an analysis of the PMP perfSONAR monitoring data that was conducted to explore the possibilities of using machine learning techniques in the future.


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