چکیده (انگلیسی):

In the period of BigData, massive amounts of structured and
unstructured data are being created every day by a multitude of everpresent
sources. BigData is complicated to work with and needs
extremely parallel software executing on a huge number of computers.
MapReduce is a current programming model that makes simpler writing
distributed applications which manipulate BigData. In order to make
MapReduce to work, it has to divide the workload between the computers
in the network. As a result, the performance of MapReduce vigorously
depends on how consistently it distributes this study load. This can be a
challenge, particularly in the arrival of data skew. In MapReduce,
workload allocation depends on the algorithm that partitions the data.
How consistently the partitioner distributes the data depends on how huge
and delegate the sample is and on how healthy the samples are examined
by the partitioning method. This study recommends an enhanced
partitioning algorithm using modified key partitioning that advances load
balancing and memory utilization. This is completed via an enhanced
sampling algorithm and partitioner. To estimate the proposed algorithm,
its performance was compared against a high-tech partitioning mechanism
employed by TeraSort. Experimentations demonstrate that the proposed
algorithm is quicker, more memory efficient and more accurate than the
existing implementation.