Configuration knobs of database systems are essential to achieve high throughput and low
latency. Recently, automatic tuning systems using machine learning methods (ML) have …

Finding good cloud configurations for deploying a single distributed system is already a
challenging task, and it becomes substantially harder when a data analytics cluster is …

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 …

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 …

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 …

Distributed dataflow systems enable data-parallel processing of large datasets on clusters.
Public cloud providers offer a large variety and quantity of resources that can be used for …

Distributed dataflow systems enable the use of clusters for scalable data analytics. However,
selecting appropriate cluster resources for a processing job is often not straightforward …

One of the key challenges for data analytics deployment is configuration tuning. The existing
approaches for configuration tuning are expensive and overlook the dynamic characteristics …

When there is a need to make an ultimate decision about the unique features of big data
platforms, one should note that they have configurable parameters. Apache Spark is an …

Distributed dataflow systems like Spark and Flink enable the use of clusters for scalable data
analytics. While runtime prediction models can be used to initially select appropriate cluster …