Heterogeneous Phase-Level Scheduling With Jobs Execution Scheduling Algorithm To Enhance Job Execution And Resource Management In Mapreduce (International Journal of Engineering and Computer Science, 2016)
In Big Data, Map Reduce is a technique that helps to process the query from user to server in an efficient way. The Map Reduce is used to process large amount of servers in a parallel way. Hence the parallel processing is done in map reduce to retrieve results. To achieve this parallel processing, the jobs are split into 3 phases. Each phase is provided with resources for parallel and fast execution of jobs. If the resources are provided in a homogeneous way, it takes more time to complete a task.Now Heterogeneous phase level scheduling algorithm with Jobs Execution Scheduling is used to split the resources in heterogeneous way. This helps to achieve jobs to be execute greater with effective use of resources which improves speed and showing the resource usage variability within the lifetime of a task using a wide-range of Map Reduce jobs. This Scheduler improves execution parallelism and resource utilization without introducing stragglers. Energy-Efficient Algorithm provides the flow time of a job. Flow time of a job is the length of the time interval between the release time and the completion time of the job with work efficiency.