Applying big data paradigms to a large scale scientific workflow: Lessons learned and future directions
S Caíno-Lores, A Lapin, J Carretero, P Kropf - Future Generation Computer …, 2020 - Elsevier
The increasing amounts of data related to the execution of scientific workflows has raised
awareness of their shift towards parallel data-intensive problems. In this paper, we deliver
our experience combining the traditional high-performance computing and grid-based
approaches with Big Data analytics paradigms, in the context of scientific ensemble
workflows. Our goal was to assess and discuss the suitability of such data-oriented
mechanisms for production-ready workflows, especially in terms of scalability. We focused …
awareness of their shift towards parallel data-intensive problems. In this paper, we deliver
our experience combining the traditional high-performance computing and grid-based
approaches with Big Data analytics paradigms, in the context of scientific ensemble
workflows. Our goal was to assess and discuss the suitability of such data-oriented
mechanisms for production-ready workflows, especially in terms of scalability. We focused …
[PDF][PDF] (2020). Applying big data paradigms to a large scale scientific workflow: Lessons learned and future directions. Future Generation Computer Systems, 110, pp …
S Caíno-Lores, A Lapin, J Carretero, P Kropf - 2020 - e-archivo.uc3m.es
The increasing amounts of data related to the execution of scientific workflows has raised
awareness of their shift towards parallel data-intensive problems. In this paper, we deliver
our experience combining the traditional high-performance computing and grid-based
approaches with Big Data analytics paradigms, in the context of scientific ensemble
workflows. Our goal was to assess and discuss the suitability of such data-oriented
mechanisms for production-ready workflows, especially in terms of scalability. We focused …
awareness of their shift towards parallel data-intensive problems. In this paper, we deliver
our experience combining the traditional high-performance computing and grid-based
approaches with Big Data analytics paradigms, in the context of scientific ensemble
workflows. Our goal was to assess and discuss the suitability of such data-oriented
mechanisms for production-ready workflows, especially in terms of scalability. We focused …
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