The quality of machine learning (ML) and deep learning (DL) models are very sensitive to
many different adjustable parameters that are set before training even begins, commonly …

Spark is one of the prevalent big data analytical platforms. Configuring proper resource
provision for Spark jobs is challenging but essential for organizations to save time, achieve …

In recent years, Apache Spark has become the de facto standard for big data processing.
Spark has enabled a wide audience of users to process petabyte-scale workloads due to its …

Spark has been increasingly employed by industries for big data analytics recently, due to its
resilience, scalability and efficient in-memory distributed programming model. Meanwhile …

The increasing popularity of Apache Spark has attracted many users to put their data into its
ecosystem. On the other hand, it has been witnessed in the literature that Spark is slow …

In memory data analytic frameworks like Apache Spark are employed by an increasing
number of diverse applications-such as machine learning, graph computation, and scientific …

Exponential growth in the volume of data generated over the last decade has triggered
massive research and adoption of distributed big data analytics platforms. In real-time …

Distributed analytics engines such as Spark are a common choice for processing extremely
large datasets. However, finding good configurations for these systems remains challenging …

Apache Spark provides a large number of configuration settings that may be tuned to
improve the performance of specific applications running on the platform. However, it is non …

Sheer increase in volume of data over the last decade has triggered research in cluster
computing frameworks that enable web enterprises to extract big insights from big data …