https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Hampton_et_al_2019aHampton et al 2019a - Revision history2026-04-22T01:07:16ZRevision history for this page on the wikiMediaWiki 1.27.0-wmf.10https://www.scipedia.com/wd/index.php?title=Hampton_et_al_2019a&diff=209173&oldid=prevScipediacontent: Scipediacontent moved page Draft Content 867629799 to Hampton et al 2019a2021-02-03T20:52:15Z<p>Scipediacontent moved page <a href="/public/Draft_Content_867629799" class="mw-redirect" title="Draft Content 867629799">Draft Content 867629799</a> to <a href="/public/Hampton_et_al_2019a" title="Hampton et al 2019a">Hampton et al 2019a</a></p>
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</td></tr></table>Scipediacontenthttps://www.scipedia.com/wd/index.php?title=Hampton_et_al_2019a&diff=209172&oldid=prevScipediacontent: Created page with " == Abstract == REANA is a reusable and reproducible data analysis platform allowing researchers to structure their analysis pipelines and run them on remote containerised co..."2021-02-03T20:52:12Z<p>Created page with " == Abstract == REANA is a reusable and reproducible data analysis platform allowing researchers to structure their analysis pipelines and run them on remote containerised co..."</p>
<p><b>New page</b></p><div><br />
== Abstract ==<br />
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REANA is a reusable and reproducible data analysis platform allowing researchers to structure their analysis pipelines and run them on remote containerised compute clouds. REANA supports several different workflows systems (CWL, Serial, Yadage) and uses Kubernetes' job execution backend. We have designed an abstract job execution component that extends the REANA platform job execution capabilities to support multiple compute backends. We have tested the abstract job execution component with HTCondor and verified the scalability of the designed solution. The results show that the REANA platform would be able to support hybrid scientific workflows where different parts of the analysis pipelines can be executed on multiple computing backends.<br />
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== Original document ==<br />
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The different versions of the original document can be found in:<br />
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* [http://cds.cern.ch/record/2695179 http://cds.cern.ch/record/2695179]<br />
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* [http://cds.cern.ch/record/2696223 http://cds.cern.ch/record/2696223]<br />
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* [http://cds.cern.ch/record/2696223/files/CERN-IT-2019-004.pdf http://cds.cern.ch/record/2696223/files/CERN-IT-2019-004.pdf]<br />
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* [http://cds.cern.ch/record/2695179/files/Poster-2019-942.pdf http://cds.cern.ch/record/2695179/files/Poster-2019-942.pdf],<br />
: [https://dblp.uni-trier.de/db/conf/eScience/eScience2019.html#MaciulaitisSBHH19 https://dblp.uni-trier.de/db/conf/eScience/eScience2019.html#MaciulaitisSBHH19],<br />
: [http://cds.cern.ch/record/2695179 http://cds.cern.ch/record/2695179],<br />
: [https://academic.microsoft.com/#/detail/3011377832 https://academic.microsoft.com/#/detail/3011377832]<br />
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* [http://xplorestaging.ieee.org/ielx7/9036011/9041688/09041809.pdf?arnumber=9041809 http://xplorestaging.ieee.org/ielx7/9036011/9041688/09041809.pdf?arnumber=9041809],<br />
: [http://dx.doi.org/10.1109/escience.2019.00091 http://dx.doi.org/10.1109/escience.2019.00091]</div>Scipediacontent