Hybrid Cloud Scaleout: Orchestrating Workloads with GitLab

6 pages•Published: September 20, 2022

Abstract

The project The FAIR Data Spaces project aims to create a common cloud-based data space for industry and research by connecting services already created in Gaia-X, IDS, NFDI and EOSC, and demonstrates this in its own demonstrators. The demonstrator FAIR Data Quality Analysis and Workflows is giving researchers a platform to define and run workflows for FAIR data, the demonstrator aims to serve as a showcase for a hybrid cloud scale-out scenario. While running user defined workflows on research data stored in git repositories, the essence of the demonstrator is hiding the technical complexity of the hybrid cloud scale-out that is needed to supply the computational power for running the workflow steps. In order to achieve this the demonstrator uses state-of- the-art cloud technologies combined with the most recent developments from the Gaia- X frameworks.

Keyphrases: fair data, hybrid cloud, open data, open science, public cloud, reproducibility, scale out

In: Jean-François Desnos, Ramin Yahyapour and Raimund Vogl (editors). Proceedings of EUNIS 2022 – The 28th International Congress of European University Information Systems, vol 86, pages 50-55.

Links:	https://easychair.org/publications/paper/bXV5
	https://doi.org/10.29007/nwh7

BibTeX entry

@inproceedings{EUNIS2022:Hybrid_Cloud_Scaleout_Orchestrating,
  author    = {Marius Politze},
  title     = {Hybrid Cloud Scaleout: Orchestrating Workloads with GitLab},
  booktitle = {Proceedings of EUNIS 2022 –  The 28th International Congress of European University Information Systems},
  editor    = {Jean-François Desnos and Ramin Yahyapour and Raimund Vogl},
  series    = {EPiC Series in Computing},
  volume    = {86},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/bXV5},
  doi       = {10.29007/nwh7},
  pages     = {50-55},
  year      = {2022}}

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