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.

在过去的十年中，分布式和并行计算取得了一些进步。每天从各种来源生成大量数据，这种快速的数据扩散导致开发了许多可以有效处理如此大数据的框架，例如Microsoft Dryad，Apache Hadoop等。Apache Hadoop是一个开源应用程序并获得了众多研究人员的广泛关注。为了更好的性能，需要对作业进行正确的调度。在开发现有的MapReduce调度程序以及开发新的优化技术或算法方面已经做出了许多努力。本文重点介绍Hadoop MapReduce框架，其缺点，在将作业调度到节点上时我们面临的各种问题以及各种研究人员提出的算法。此外，