Assessment of Various MapReduce Scheduling in Heterogeneous Environment

There have been numerous developments in distributed and parallel computing over the past decade. A huge quantity of data is created every day from a number of sources, and the increasing dissemination of data has led to the creation of several frameworks capable of efficiently managing such massive data. Apache Hadoop is a software component of Google MapReduce that has attracted the interest of numerous researchers. For improved performance, proper scheduling of tasks is necessary. Many efforts have gone into the creation of existing MapReduce schedulers as well as the development of new efficient procedures. The assessment of various scheduling algorithms has been presented. In addition, we classify these algorithms based on a variety of quality parameters that influence MapReduce performance.