MapReduce: A Technical Review

Technical Report: MapReduce-based Similarity Joins

Cloud enabled systems have become a crucial component to efficiently process and analyze massive amounts of data. One of the key data processing and analysis operations is the Similarity Join, which retrieves all data pairs whose distances are smaller than a predefined threshold ε. Even though multiple algorithms and implementation techniques have been proposed for Similarity Joins, very little...

A Review on HADOOP MAPREDUCE-A Job Aware Scheduling Technology

A Review of CUDA, MapReduce, and Pthreads Parallel Computing Models

The advent of high performance computing (HPC) and graphics processing units (GPU), present an enormous computation resource for Large data transactions (big data) that require parallel processing for robust and prompt data analysis. While a number of HPC frameworks have been proposed, parallel programming models present a number of challenges – for instance, how to fully utilize features in th...

A Review: Mapreduce and Spark for Big Data Analytics

In this paper we discuss the various challenges of Big Data and problem arises due to continuous explosion of data resulting from the likes of social media and other online sources to gain access to deeper analysis of their data. This paper discusses two of the comparison of Hadoop Map Reduce and the recently introduced Apache Spark – both of which provide a processing model for analyzing big d...

A Comprehensive Review of Straggler Handling Algorithms for MapReduce Framework

Distributed computing accomplished broad appropriation because of consequently parallelizing and transparently executing tasks in distributed environments. Straggling tasks is an essential test confronted by all Big Data Processing Frameworks for example MapReduce, Dryad, and Spark. Stragglers are the assignments that run much slower than different tasks and since a job completes just when it’s...