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 …

Choosing the right parameter configurations for recurring jobs running on big data analytics
platforms is difficult because there can be hundreds of possible parameter configurations to …

Data of the order of terabytes, petabytes, or beyond is known as Big Data. This data cannot
be processed using the traditional database software, and hence there comes the need for …

In the realm of big data, where datasets of immense scale pose processing challenges,
distributed processing platforms like open-source Apache Spark have emerged to address …

As data is growing in different dimensions, it is difficult to get appropriate data analytic tools.
Spark is one of high speed" in-memory computing" big data analytic tool designed to …

Abstract Systems and methods are provided for optimally allocating resources used to
perform multiple tasks/jobs, eg, machine learning training jobs. The possible resource …

An increasing number of big data applications in various domains generate datasets
continuously, which must be processed for various purposes in a timely manner. As one of …

Apache spark is one of the high speed" in-memory computing" that run over the JVM. Due to
increasing data in volume, it needs performance optimization mechanism that requires …

With the increase in the adoption of Cloud computing and big data processing systems, a
common way to deploy analytics workloads is to acquire on-demand resources in a Cloud …

Modern IT systems offer hundreds of tunable knobs that impact their performances. Manually
finding a well-performing configuration is a daunting task, especially when considering that …