Cloud Data Storage Federation for Scientific Applications

Authors
Publication date 2014
Host editors
  • D. an Mey
  • M. Alexander
  • P. Bientinesi
  • M. Cannataro
  • C. Clauss
  • A. Costan
  • G. Kecskemeti
  • C. Morin
  • L. Ricci
  • J. Sahuquillo
  • M. Schulz
  • V. Scarano
  • S.L. Scott
  • J. Weidendorfer
Book title Euro-Par 2013: Parallel Processing Workshops
Book subtitle BigDataCloud, DIHC, FedICI, HeteroPar, HiBB, LSDVE, MHPC, OMHI, PADABS, PROPER, Resilience, ROME, and UCHPC 2013, Aachen, Germany, August 26-27, 2013 : revised selected papers
ISBN
  • 9783642544194
ISBN (electronic)
  • 9783642544200
Series Lecture Notes in Computer Science
Event Euro-Par / BigDataCloud 2013
Pages (from-to) 13-22
Publisher Heidelberg: Springer
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract
Nowadays, data-intensive scientific research needs storage capabilities that enable efficient data sharing. This is of great importance for many scientific domains such as the Virtual Physiological Human. In this paper, we introduce a solution that federates a variety of systems ranging from file servers to more sophisticated ones used in clouds or grids. Our solution follows a client-centric approach that loosely couples a variety of data resources that may use different technologies such as Openstack-Swift, iRODS, GridFTP, and may be geographically distributed. It is implemented as a lightweight service which does not require installation of a software on the resources it uses. In this way we are able to efficiently use heterogeneous storage resources, reduce the usage complexity of multiple storage resources, and avoid vendor lock-in in case of cloud storage. To demonstrate the usability of our approach we performed a number of experiments that assess the performance and functionality of the developed system.
Document type Conference contribution
Language English
Published at https://doi.org/10.1007/978-3-642-54420-0_2
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