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Empirical Study of Job Scheduling Algorithms in Hadoop MapReduce
2017
Cybernetics and Information Technologies
Several Job scheduling algorithms have been developed for Hadoop-Map Reduce model, which vary widely in design and behavior for handling different issues such as locality of data, user share fairness, and resource awareness. This article focuses on empirically evaluating the performance of three schedulers: First In First Out (FIFO), Fair scheduler, and Capacity scheduler. To carry out the experimental evaluation, we implement our own Hadoop cluster testbed, consisting of four machines, in
doi:10.1515/cait-2017-0012
fatcat:kocust3jjfcqjl4thmkbcouiii