Open AccessMakespan Map Reduce Architecture for Efficient Memory Utilization
Author(s) -
Archana Bhaskar*,
Dr.Rajeev Raman
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c4729.098319
Subject(s) - computer science , job shop scheduling , overhead (engineering) , cloud computing , distributed computing , process (computing) , scheme (mathematics) , big data , execution time , parallel computing , real time computing , embedded system , data mining , operating system , routing (electronic design automation) , mathematical analysis , mathematics
Makespan is referred to the total execution time taken to process the tasks or the jobs to the completion time.The High performance infrastructure in cloud computing provides extensive applications. These applications are preferred in Big Data. The existing Hadoop Map Reduce network incurs the input output memory overhead. The parallel Map Reduce network provides a parallel scheme to reduce makespan times in computing environments. The outcome provides improvement in the coefficient correlation and makespan time. The various challenges in computing dataset is handling large dataset efficiently and providing large amount of datasets with ease. The comprehensive method is to enhance data analyzation techniques. Massive large amount of data which are spread across the large number of machines needs to be parallelized