The advent of social networking and internet of things (IoT) has resulted in exponential growth of data in the last few years. This, in turn, has increased the need to process and analyze such data for optimal decision making. In order to achieve better results, there is an emergence of newly-built architectures for parallel processing. Hadoop MapReduce (MR) is a programming model that is considered as one of the most powerful computation tools for processing the data on a given cluster of commodity nodes. However, the management of clusters along with various quality requirements necessitates the use of efficient MR scheduling. The chapter discusses the classification of MR scheduling algorithms based on their applicability with required parameters of quality of service (QoS). After classification, a detailed study of MR schedulers has been presented along with their comparison on various parameters.