Abstract
Big data is being generated everywhere around us at all times by cameras, mobile devices, sensors, and software logs with large amount of data in units of hundreds of terabytes to petabytes. Therefore, to analyse these massive data, new skills, intensive applications and storage clusters are needed. Apache Hadoop is one of the most recently popular tools developed for big data processing. The main purpose in this paper is to analyse different scheduling algorithms that can help to achieve better performance, efficiency and reliability of Hadoop YARN environment. We describe some task schedulers which consider different levels of Hadoop such as first in first out (FIFO) scheduler, fair scheduler, delay scheduler, deadline constraint scheduler, dynamic priority scheduling, capacity scheduler, and we analyse the performance of these widely used Hadoop task schedulers based on the following elements: makespan; turnaround time; and throughput. To conclude this paper, the experimental results were given.
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