A unit-based, cost-efficient scheduler for heterogeneous Hadoop systems.

A significant amount of research in the field of job scheduling is carried out in Hadoop. However, there is still need for research to overcome some challenges regarding scheduling jobs in Hadoop clusters. There are various factors affecting the performance of scheduling policies like data volume (storage), data source format (different data), speed (data rate), security and privacy, cost, connection and data sharing. To reach a better utilization of resources and managing big data, scheduling policies have been designed. In this paper, an algorithm has been presented that can run on heterogeneous Hadoop clusters and runs job in parallel. This algorithm first distributes data based on the performance of the nodes and then schedules the jobs according to their cost of execution and decreases the cost of executing the jobs. The presented algorithm offers better performance in terms of execution time, cost and locality compared to FIFO and Fair schedulers. [ABSTRACT FROM AUTHOR]