Analysis of Job Scheduling Algorithms for Dynamic Job Ordering Optimization

  • praveen Raut

Abstract

 MapReduce and Hadoop are used in many applications like clinical data analysis, Facebook, amazon etc., in which big data processing and utilization is required. MapReduce automatically uses parallelize computation for large datasets by running multiple map and reduce tasks by distributing data across multiple machines. Different algorithms used for job scheduling in MapReduce which used for Resource utilization and job allocation.  Researchers have proposed many new MapReduce schedulers according to applications, but there are still several major drawbacks that are still not well studied.  In this paper, by analyzing and studding different job scheduling algorithms so we can optimize MapReduce performance. The study discussed the most popular and efficient systems design to the improvements in MapReduce workload and reviewed the corresponding solutions. In proposed method, we are applying PRISM algorithm for job scheduling which helps to resource utilization and reduce time.Keywords: MapReduce, Hadoop, Scheduling algorithm, Job ordering, PRISM, Resource utilization.

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Published
2016-11-16
How to Cite
RAUT, praveen. Analysis of Job Scheduling Algorithms for Dynamic Job Ordering Optimization. International Journal Of Emerging Technology and Computer Science, [S.l.], v. 1, n. 4, nov. 2016. ISSN 2455-9954. Available at: <https://aspirepublishers.com/index.php/ijetcs/article/view/67>. Date accessed: 31 may 2020.