Towards optimizing the execution of spark scientific workflows using machine learning‐based parameter tuning

Summary In the last few years, Apache Spark has become a de facto the standard framework for big data systems on both industry and academy projects. Spark is used to execute compute‐ and data‐intensive workflows in distinct areas like biology and astronomy. Although Spark is an easy‐to‐install framework, it has more than one hundred parameters to be set, besides domain‐specific parameters of each workflow. In this way, to execute Spark‐based workflows efficiently, the user has to fine‐tune 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‐and‐error manner since it is tedious and error‐prone. 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‐specific ones related to the workflow performance in the predictive model.