Extending MapReduce with Bag-Of-Task for Scheduling Cloud Jobs

MapReduce is a powerful platform for large-scale data processing. MapReduce provides an easy parallel programming interface in a distributed computing environment. Bag-of-Tasks (BoTs) are parallel applications with no inter-task communication. Different approaches are studied to reduce the execution time and maximize the CPU utilization for cloud jobs. According to the studies, this can be happened MapReduce with BOT. Moreover, there are large numbers of jobs and if they are not scheduled well then complexity of execution will be occur. So, by applying scheduling techniques like MapReduce and BOT on cloud jobs this can be avoided. In Mapreducer’s reducer phase cloud jobs are going to executes serial so it takes too much execution time and minimum CPU utilization but after applying BOT on that phase jobs are going to execute parallel so execution time will reduced and CPU utilization will be maximized. From this approach, execution time may be reduced for cloud jobs and CPU utilization may be maximized.