Paper Details

The Rapid improvement in the computational speed of the technology has made our life easier. Newtechnologies like parallel computing and distributed computing has made a significant improvement in the speed ofcomputing and help us to solve many complicated issues. The map reduce which is used in parallel computing is one ofthe popular data model for high speed computation in computation technology. The Existing map reduce focuses onscheduling at the task-level. But unfortunately, the task-level scheduling leads to inefficient job schedules with lowresource utilization and long job execution time. In this concept we divide the tasks into unequal parts called as phasesand apply phase-level scheduling to these phases and achieve efficient resource usage.A fine-grained, phase and resource-aware Map Reduce Scheduler was introduced that divides tasks intophases, where each phase has a constant resource usage profile, and performs scheduling at the phase level. System canprovide accurate resource information that can be used by the scheduler so that it can take effective scheduling decisionsand reduce the job execution time. The phase-level scheduling achieves higher resource utilization when compared totask level schedulers

Nisha Shinde, Trupti Patil, Prajkta Shinde, Himani Mavchi, “Efficient Job Execution for Map Reduce Using Phase-Level Scheduling Algorithm.”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 12, pp. -, December 2017.