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Spark is used to execute compute\u2010 and data\u2010intensive workflows in distinct areas like biology and astronomy. Although Spark is an easy\u2010to\u2010install framework, it has more than one hundred parameters to be set, besides domain\u2010specific parameters of each workflow. In this way, to execute Spark\u2010based workflows efficiently, the user has to fine\u2010tune a myriad of Spark and workflow parameters (eg, partitioning strategy, the average size of a DNA sequence, etc.). This configuration task cannot be manually performed in a trial\u2010and\u2010error manner since it is tedious and error\u2010prone. This article proposes an approach that focuses on generating interpretable predictive machine learning models (ie, decision trees), and then extract useful rules (ie, patterns) from these models that can be applied to configure parameters of future executions of the workflow and Spark for nonexperts users. In the experiments presented in this article, the proposed parameter configuration approach led to better performance in processing Spark workflows. Finally, the approach introduced here reduced the number of parameters to be configured by identifying the most relevant domain\u2010specific ones related to the workflow performance in the predictive model.<\/jats:p>","DOI":"10.1002\/cpe.5972","type":"journal-article","created":{"date-parts":[[2020,9,5]],"date-time":"2020-09-05T07:26:29Z","timestamp":1599290789000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Towards optimizing the execution of spark scientific workflows using machine learning\u2010based parameter tuning"],"prefix":"10.1002","volume":"33","author":[{"given":"Douglas","family":"de Oliveira","sequence":"first","affiliation":[{"name":"Instituto de Computa\u00e7\u00e3o Universidade Federal Fluminense  Niter\u00f3i Brazil"},{"name":"DexlLab \u2010 Data Extreme Lab Laborat\u00f3rio Nacional de Computa\u00e7\u00e3o Cient\u00edfica  Petr\u00f3polis Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F\u00e1bio","family":"Porto","sequence":"additional","affiliation":[{"name":"DexlLab \u2010 Data Extreme Lab Laborat\u00f3rio Nacional de Computa\u00e7\u00e3o Cient\u00edfica  Petr\u00f3polis Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cristina","family":"Boeres","sequence":"additional","affiliation":[{"name":"Instituto de Computa\u00e7\u00e3o Universidade Federal Fluminense  Niter\u00f3i Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"de Oliveira","sequence":"additional","affiliation":[{"name":"Instituto de Computa\u00e7\u00e3o Universidade Federal Fluminense  Niter\u00f3i Brazil"},{"name":"UFFeScience \u2010 Virtual Laboratory of eScience Universidade Federal Fluminense  Niter\u00f3i Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2020,9,5]]},"reference":[{"volume-title":"The Fourth Paradigm: Data\u2010Intensive Scientific Discovery","year":"2009","author":"Hey T","key":"e_1_2_9_2_1"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409360.1409366"},{"issue":"12","key":"e_1_2_9_4_1","first-page":"2082","article-title":"DfAnalyzer: runtime dataflow analysis of scientific applications using provenance","volume":"11","author":"Silva V","year":"2018","journal-title":"PVLDB"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3184900"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-22825-4_9"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1017833.1017862"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1615\/Int.J.UncertaintyQuantification.v2.i1.50"},{"key":"e_1_2_9_9_1","doi-asserted-by":"crossref","unstructured":"SilvaV SouzaR CamataJ et al. 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