Abstract
MapReduce-as-a-Service cloud is of great importance because of the data growth and increase in opportunities in big data analytics. MapReduce platforms provided through cloud help the end user by providing ready to use MapReduce clusters. Since the cloud environment is virtualized, allocating Virtual Machines (VMs) efficiently has high relevance. If the VMs allocated for a MapReduce cluster are hosted in distant Physical Machines (PMs), the interaction between VMs causes delays depending upon the distance between the PMs hosting them. In this paper, we propose a heuristic algorithm for VM allocation for providing MapReduce as a cloud service. This algorithm allocates VMs in same or nearby PMs and hence reduces data transfer delay between VMs. Simulation results demonstrate the improvement on execution time of the VM allocation algorithm without compromising the performance of applications running on the allocated VMs.
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