Job Attentive Scheduling Algorithm in Hadoop

In recent years cloud services have gained much attention as a result of their availability, scalability, and low cost. One use of these services has been for the execution of scientific workflows as part of Big Data Analytics, which are employed in a diverse range of fields including astronomy, physics, seismology, and bioinformatics. There has been much research on heuristic scheduling algorithms for these workflows due to the problem’s inherent complexity, however existing work has mainly considered execution in a utility grid environment using a generic distributed framework. For our research, we consider the ever-increasingly popular Apache Hadoop framework for scheduling workflows onto resources rented from cloud service providers. Contrary to other distributed frameworks, the Hadoop MapReduce model imposes a functional style onto application definition, and as such presents an interesting and unapproached challenge for workflow scheduling. Investigated in our work heterogeneous approach scheduling on the Hadoop MapReduce platform, to minimize workflow makespan while satisfying a given budget constraint. Keywords— Scheduling Algorithms FIFO, Fair Scheduling, Capacity Scheduling, Hybrid Scheduler Map Reduce Model, Modified Algorithm, Result analysis.