Empirical Evaluation of Acquisition Functions for Bayesian Optimization-based Configuration Tuning of Apache Spark Applications

Abstract

The execution time of an Apache Spark application is heavily influenced by its configuration settings. Accordingly, Bayesian Optimization (BO) is commonly used for automated tuning, employing the acquisition function, Expected Improvement (EI). However, existing works did not compare the performance to the other acquisition functions empirically. In this paper, we show that EI may not work well for Spark applications due to a huge search space compared to the other optimization problems. In addition, we demonstrate the performance of BO based on Probability of Improvement (PI), which achieves exploration via rich random initialization and exploitation via the PI acquisition function. Through the experimental evaluations, we show that the PI-based BO outperforms the EI-based BO in both optimal time and optimization cost.