Skip to content

A Performance Prediction Model for Spark Applications

Apache Spark is a popular open-source distributed-processing framework that enables efficient processing of massive amounts of data. It has a large number of parameters that need to be tuned to get the best performance. However, tuning these parameters manually is a complex and time-consuming task. Therefore, a robust performance model to predict applications execution time could greatly help in accelerating the deployment and optimization of big data applications relying on Spark. In this paper, we ran extensive experiments on a selected set of Spark applications that cover the most common workloads to generate a representative dataset of execution time. In addition, we extracted application and data features to build a machine learning-based performance model to predict Spark applications execution time. The experiments show that boosting algorithms achieved better results compared to other algorithms.

Florian Demesmaeker, Amine Ghrab, Usama Javaid, Ahmed Amir Kanoun, A Performance Prediction Model for Spark Applications, in the proceedings of Big Data congress 2020.

Click here to access the paper in its preprint form.

Releated Posts

We Collaborate on the TAUDoS Project

We started a new collaboration with Aix-Marseille University, Montreal University, Nantes University, and St-Etienne on a four-year project called TAUDoS, which focuses on Trustful AI.
Read More

DEBS 2022

In June 2022, our research director Sabri Skhiri and the head of the data science department at Madalina Ciortan travelled to Copenhagen to attend DEBS 2022, the leading conference focusing on distributed and event-based systems.
Read More