• Hareesh Edukoju
• Naveen Kumar N

In today’s world the amount of data being generated is growing exponentially. A number of these data are structured, semi-structured or unstructured. This poses an excellent challenge once these information are to be analyzed as a result of conventional data processing techniques are not suited to handling such information. Map reduce may be a programming model and an associated implementation for process and generating massive information sets. A Map reduce workload usually contains a group of jobs, every of that consists of multiple map tasks followed by multiple reduce tasks. This technique proposes of algorithms to optimize the Makespan and also the total completion time for an offline MapReduce workload. Our algorithms concentrate on the task ordering optimization for a MapReduce workload and that we will perform optimization of Makespan and total completion for a MapReduce workload. Our work is focuses on resolution the time efficiency issues still as memory utilization problem. By using MK_TCT_JR algorithm made the result that are up to, 90 to fix things than MK_JR. Our algorithm can improve the system performance in terms of Makepan and total completion time.

Mapreduce, Hadoop, Scheduling Algorithm, Job Ordering, Makespan, Total Completion Time, Resource Allocation Mapreduce Slot Allocation

"Minimizing the MakeSpan of Multiple MapReduce Jobs through Job Ordering Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.4, Issue 9, page no.302-304, September-2017, Available :http://www.jetir.org/papers/JETIR1709049.pdf

"Minimizing the MakeSpan of Multiple MapReduce Jobs through Job Ordering Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.4, Issue 9, page no. pp302-304, September-2017, Available at : http://www.jetir.org/papers/JETIR1709049.pdf