Efficient Job Execution for Map Reduce Using Phase-Level Scheduling Algorithm.

Abstract

The Rapid improvement in the computational speed of the technology has made our life easier. New
technologies like parallel computing and distributed computing has made a significant improvement in the speed of
computing and help us to solve many complicated issues. The map reduce which is used in parallel computing is one of
the popular data model for high speed computation in computation technology. The Existing map reduce focuses on
scheduling at the task-level. But unfortunately, the task-level scheduling leads to inefficient job schedules with low
resource utilization and long job execution time. In this concept we divide the tasks into unequal parts called as phases
and 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 into
phases, where each phase has a constant resource usage profile, and performs scheduling at the phase level. System can
provide accurate resource information that can be used by the scheduler so that it can take effective scheduling decisions
and reduce the job execution time. The phase-level scheduling achieves higher resource utilization when compared to
task level schedulers