Highly Parallel Map Reduce Process and Efficient Job Scheduling Methodologies of Big Data Systems
Suja Cherukullapurath Mana1, T. Sasipraba2
1Suja Cherukullapurath Mana, School of Computing, Sathyabama Institute of Science and Technology, Chennai, India.
2T. Sasipraba, School of Computing, Sathyabama Institute of Science and Technology, Chennai, India.
Manuscript received on October 15, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 3394-3397 | Volume-9 Issue-1, November 2019. | Retrieval Number: A3903119119/2019©BEIESP | DOI: 10.35940/ijitee.A3903.119119
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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: This paper studies about various job scheduling methodologies used in big data systems. Map reduce is a highly efficient distributed job processing strategy for big data systems. Job scheduling is a critical task of any big data system as the volume of jobs need to be processed is tremendous. This study will go over the map reduce process in detail. It also reviews various job scheduling methodologies and tries to perform an efficient comparison among these methodologies.
Keywords: Big data, Job Scheduling, Resource Allocation.
Scope of the Article: Big data