For frequent disk I/O and large data transmissions among different racks and physical
nodes, intermediate data communication has become the most important performance bottle …

Recent years have seen an increasing number of scientists employ data parallel computing
frameworks such as MapReduce and Hadoop to run data intensive applications and …

Hadoop has been regarded as the de-facto standard for handling data-intensive distributed
applications with its popular storage and processing engine called as the Hadoop …

Data placement decision of Hadoop distributed file system (HDFS) is very important for the
data locality which is a primary criterion for task scheduling of MapReduce model and …

Enterprises increasingly use the Hadoop Distributed File System (HDFS) to manage and
store big data for many applications. However, HDFS uses triple replication, leading to …

Recent years have seen an increasing number of scientists employing data parallel
computing frameworks, such as Hadoop, in order to run data-intensive applications …

This article describes how the time taken to execute a query and return the results, increase
exponentially as the data size increases, leading to more waiting times of the user. Hadoop …

One of the key challenges in big data technology is the velocity at which the data is
processed. Hadoop, an open-source software framework, is the dominant technology to …

Abstract The execution of Map-Reduce applications on the Hadoop cluster poses significant
challenges due to the non-consideration of data locality, ie, assigning tasks to compute …

To address the challenging needs of high‐performance big data processing, parallel‐
distributed frameworks such as Hadoop are being utilized extensively. However, in …