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Efficient execution of scientific workflows in the cloud through adaptive caching

Heidsieck Gaetan, De Oliveira Daniel, Pacitti Esther, Pradal Christophe, Tardieu François, Valduriez Patrick. 2020. Efficient execution of scientific workflows in the cloud through adaptive caching. In : Transactions on large-scale data- and knowledge-centered systems XLIV. Hameurlain Abdelkader (ed.), Tjoa A. Min (ed.), Lamarre Philippe (ed.), Zeitouni Karine (ed.). Berlin : Springer, pp. 41-66. (Lecture Notes in Computer Science, 12380) ISBN 978-3-662-62270-4

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Abstract : The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing (e.g., computing resources, services, metadata, data sources) across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 44th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains six fully revised and extended papers selected from the 35th conference on Data Management – Principles, Technologies and Applications, BDA 2019. The topics covered include big data, graph data streams, workflow execution in the cloud, privacy in crowdsourcing, secure distributed computing, machine learning, and data mining for recommendation systems.

Mots-clés Agrovoc : Diffusion de l'information, information, Application des ordinateurs, Adaptabilité, données ouvertes, Projet de recherche

Mots-clés complémentaires : workflow, mise en cache, cloud (informatique), cloud computing, openalea

Mots-clés libres : OpenAlea, Adaptive Caching, Scientific workflows, Cloud computing

Classification Agris : U30 - Research methods

Auteurs et affiliations

  • Heidsieck Gaetan, INRIA (FRA)
  • De Oliveira Daniel, UFF (BRA)
  • Pacitti Esther, Université de Montpellier (FRA)
  • Pradal Christophe, CIRAD-BIOS-UMR AGAP (FRA) ORCID: 0000-0002-2555-761X
  • Tardieu François, INRAE (FRA)
  • Valduriez Patrick, INRIA (FRA)

Autres liens de la publication

Source : Cirad-Agritrop (https://agritrop.cirad.fr/596650/)

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