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Over the last decade, several advancements have happened in distributed and parallel computing. A lot of data is generated daily from various sources, and this speedy data proliferation led to the development of many more frameworks that are efficient to handle such huge data e.g. - Microsoft Dryad, Apache Hadoop, etc. Apache Hadoop is an open-source application of Google MapReduce and is getting a lot of attention from various researchers. Proper scheduling of jobs needs to be done for better performance. Numerous efforts have been done in the development of existing MapReduce schedulers and in developing new optimized techniques or algorithms. This paper focuses on the Hadoop MapReduce framework, its shortcomings, various issues we face while scheduling jobs to nodes and algorithms proposed by various researchers. Furthermore, we then classify these algorithms on various quality measures that affect MapReduce performance.
Kalia et al. (Thu,) studied this question.