• Dr.K.Nagi Reddy

Now a days, analyzing the large and unstructured data is a difficult task in many data sharing and processing areas. To process the large datasets or large data we have a most popular paradigm i.e MapReduce paradigm. A MapReduce workload generally includes a hard and fast of jobs; each of jobs consist of a couple of map and reduce tasks. To improve the performance of the MapReduce workloads, we want to beautify the makespan in addition to general final touch time of the MapReduce. In this paper, author has used two algorithms which are based on Johnson’s Rule, to enhance the performance for MapReduce workloads with job ordering and slot configuration optimization approaches. The proposed algorithms are: one for optimizing the makespan by dynamic job ordering and another for reducing the total completion time of the MapReduce by slot configuration. By using the proposed algorithms, we can optimize or mitigate the makespan of the MapReduce and also we can mitigate the total completion time for the MapReduce workloads.

MapReduce, Clusters, Job Ordering, Slot Configuration, Makespan

"Large Data Processing in Clusters and Data Centers by Optimizing Makespan and Total Completion Time of MapReduce Workloads", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 10, page no.118-122, October-2018, Available :http://www.jetir.org/papers/JETIR1810799.pdf

"Large Data Processing in Clusters and Data Centers by Optimizing Makespan and Total Completion Time of MapReduce Workloads", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 10, page no. pp118-122, October-2018, Available at : http://www.jetir.org/papers/JETIR1810799.pdf