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Adaptive caching for data-intensive scientific workflows in the cloud

Heidsieck G., De Oliveira D., Pacitti E., Pradal C., Tardieu F., Valduriez P.. 2019. In : Hartmann Sven (ed.), Küng Josef (ed.), Chakravarthy Sharma (ed.), Anderst-Kotsis Gabriele (ed.), Tjoa A. Min (ed.), Khalil Ismail (ed.). Database and expert systems applications. DEXA 2019. Cham : Springer, p. 452-466. (Lecture Notes in Computer Science, 11707). International Conference on Database and Expert Systems Applications (DEXA 2019). 30, 2019-08-26/2019-08-29, Linz (Autriche).

DOI: 10.1007/978-3-030-27618-8_33

Many scientific experiments are now carried on using scientific workflows, which are becoming more and more data-intensive and complex. We consider the efficient execution of such workflows in the cloud. Since it is common for workflow users to reuse other workflows or data generated by other workflows, a promising approach for efficient workflow execution is to cache intermediate data and exploit it to avoid task re-execution. In this paper, we propose an adaptive caching solution for data-intensive workflows in the cloud. Our solution is based on a new scientific workflow management architecture that automatically manages the storage and reuse of intermediate data and adapts to the variations in task execution times and output data size. We evaluated our solution by implementing it in the OpenAlea system and performing extensive experiments on real data with a data-intensive application in plant phenotyping. The results show that adaptive caching can yield major performance gains, e.g., up to 120.16% with 6 workflow re-executions.

Mots-clés : système d'information; données; recherche; phénotype; informatique

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