Performance Enhancement of MapReduce Framework in Big Data Application Using Load Balancing with Cache

Sushant Shirish Nagavkar, Ashishkumar

Abstract: Hadoop is open source software that is used to store big data, it supports data demanding applications and performs analysis, using a random placement method for parallel processing to give effortlessness and load balance. To achieve maximum parallelism per group to load balance a new Data-gRouping-AWare (DRAW) data placement is used. Problem in big data is when any query executes repeatedly it repeats whole process of execution to obtain result. In MapReduce framework and generates a large amount of intermediate data. Such huge amount of information is thrown away after the tasks finish, because MapReduce is not able to use this data. Dache, a data-aware cache framework for big-data applications gives the produced intermediate results to the cache manager. Task inquiries the cache manager before performing the actual computing work.

Keywords: BEA, Big-data, caching, DACH, DRAW, Hadoop, HDFS, MapReduce

How to Cite?: Sushant Shirish Nagavkar, Ashishkumar, "Performance Enhancement of MapReduce Framework in Big Data Application Using Load Balancing with Cache", Volume 4 Issue 12, December 2015, International Journal of Science and Research (IJSR), Pages: 1661-1667, https://www.ijsr.net/getabstract.php?paperid=NOV152318, DOI: https://dx.doi.org/10.21275/NOV152318