Big data processing systems (eg, Hadoop, Spark, Storm) contain a vast number of
configuration parameters controlling parallelism, I/O behavior, memory settings, and …

Big Data analytics for storing, processing, and analyzing large-scale datasets has become
an essential tool for the industry. The advent of distributed computing frameworks such as …

In-memory cluster computing platforms have gained momentum in the last years, due to their
ability to analyse big amounts of data in parallel. These platforms are complex and difficult-to …

Spark has been established as an attractive platform for big data analysis, since it manages
to hide most of the complexities related to parallelism, fault tolerance and cluster setting from …

Spark is a more efficient distributed big data processing framework following Hadoop. It
provides users with more than 180 adjustable configuration parameters, and how to choose …

Spark SQL has been widely deployed in industry but it is challenging to tune its
performance. Recent studies try to employ machine learning (ML) to solve this problem, but …

The distributed data analytic system--Spark is a common choice for processing massive
volumes of heterogeneous data, while it is challenging to tune its parameters to achieve …

Distributed data analytic engines like Spark are common choices to process massive data in
industry. However, the performance of Spark SQL highly depends on the choice of …

Due to the rapid growth of available data, various platforms offer parallel infrastructure that
efficiently processes big data. One of the critical issues is how to use these platforms to …

Tuning configurations of Spark jobs is not a trivial task. State-of-the-art auto-tuning systems
are based on iteratively running workloads with different configurations. During the …