Distributed caching of scientific workflows in multisite cloud

Heidsieck Gaëtan, De Oliveira Daniel, Pacitti Esther, Pradal Christophe, Tardieu François, Valduriez Patrick. 2020. Distributed caching of scientific workflows in multisite cloud. In : Database and expert systems applications. DEXA 2020 (Part II). Hartmann Sven (ed.), Küng Josef (ed.), Kotsis Gabriele (ed.), Tjoa A. Min (ed.), Khalil Ismail (ed.). Cham : Springer, 51-65. (Lecture Notes in Computer Science, 12392, 12392) ISBN 978-3-030-59050-5 International Conference on Database and Expert Systems Application (DEXA 2020). 31, Bratislava, Slovaquie, 14 Septembre 2020/17 Septembre 2020.

https://doi.org/10.1007/978-3-030-59051-2_4

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Résumé : Many scientific experiments are performed using scientific workflows, which are becoming more and more data-intensive. We consider the efficient execution of such workflows in the cloud, leveraging the heterogeneous resources available at multiple cloud sites (geo-distributed data centers). Since it is common for workflow users to reuse code or data from other workflows, a promising approach for efficient workflow execution is to cache intermediate data in order to avoid re-executing entire workflows. In this paper, we propose a solution for distributed caching of scientific workflows in a multisite cloud. We implemented our solution in the OpenAlea workflow system, together with cache-aware distributed scheduling algorithms. Our experimental evaluation on a three-site cloud with a data-intensive application in plant phenotyping shows that our solution can yield major performance gains, reducing total time up to 42% with 60% of same input data for each new execution.

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

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