Makespan Map Reduce Architecture for Efficient Memory Utilization
Archana Bhaskar1, Rajeev Ranjan2
1Archana Bhaskar, Department of Computer Applications, Acharya Institute of Technology, Bangalore, Karnataka.
2Dr. Rajeev Raman, Department of Computer Applications, Reva University, Bangalore, Karnataka
Manuscript received on 13 August 2019. | Revised Manuscript received on 18 August 2019. | Manuscript published on 30 September 2019. | PP: 5878-5881 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4729098319/19©BEIESP | DOI: 10.35940/ijrte.C4729.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Index Terms: Big Data, Bioinformatics, Caching, Cloud Computing, Hadoop, MapReduce
Scope of the Article: Cloud Computing