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 …

Background: Hadoop has become the base framework on the big data system via the simple
concept that moving computation is cheaper than moving data. Hadoop increases a data …

MapReduce has emerged as a strong model for processing parallel and distributed data for
huge datasets. Hadoop an open source implementation of MapReduce has approved …

MapReduce effectively partitions and distributes computation workloads to a cluster of
servers, facilitating today's big data processing. Given the massive data to be dispatched …

MapReduce has been widely used in many data science applications. It has been observed
that an excessive data transfer has a negative impact on its performance. To reduce the …

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 …

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 …

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 …

The MapReduce model has become an important parallel processing model for large-scale
data-intensive applications like data mining and web indexing. Hadoop, an open-source …

Recent years have witnessed a surge of new generation applications involving big data. The
de facto framework for big data processing, MapReduce, has been increasingly embraced …