M VINOD KUMAR, K PRAVEEN KUMAR, K SOWMYA, P V KOMALI 01 01 Map Reduce is a popular parallel computing paradigm for largescale data processing in clusters and data centers A Map Reduce workload generally contains a set of jobs each of which consists of multiple map tasks followed by multiple reduce tasks Due to 1 that map tasks can only run in map slots and reduce tasks can only run in reduce slots and 2 the general execution constraints that map tasks are executed before reduce tasks different job execution orders and mapreduce slot configurations for a MapReduce workload have significantly different performance and system utilization This paper proposes two classes of algorithms to minimize the makespan and the total completion time for an offline MapReduce workload Our first class of algorithms focuses on the job ordering optimization for a MapReduce workload under a given mapreduce slot configuration In contrast our second class of algorithms considers the scenario that we can perform optimization for mapreduce slot configuration for a Map Reduce workload We perform simulations as well as experiments on Amazon EC2 and show that our proposed algorithms produce results that are up to 15 80 percent better than currently un optimized Hadoop leading to significant reductions in running time in practice Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026