@article {Shabeera2016,
	title = {Curtailing job completion time in MapReduce clouds through improved Virtual Machine allocation},
	journal = {Computers \& Electrical Engineering},
	year = {2016},
	note = {n/a},
	pages = {-},
	abstract = {<p>Abstract Cloud-based MapReduce platforms offer ready to use MapReduce clusters. The problem of allocating Virtual Machines (VMs) carrying out the computation, for minimizing data transfer delay is a crucial one in this context, as the MapReduce tasks are communication intensive. The interaction between \{VMs\} may face varying delays, if the \{VMs\} are hosted in different Physical Machines (PMs). This work aims to optimize the data transfer delay between VMs, which is denoted by the distance between the VMs. We propose an approximation algorithm for \{VM\} allocation in data centers wherein the distances between \{VMs\} satisfy triangular inequality and an optimization algorithm for \{VM\} allocation in data centers where the distances between \{VMs\} do not satisfy triangular inequality. Simulations on CloudSim demonstrate the performance of our algorithms and the results affirm the reduction in job completion time compared to other allocation schemes.</p>
},
	keywords = {Virtual machine allocation},
	issn = {0045-7906},
	doi = {http://dx.doi.org/10.1016/j.compeleceng.2016.10.009},
	url = {http://www.sciencedirect.com/science/article/pii/S0045790616305286},
	author = {Shabeera, TP and SD MadhuKumar and Priya Chandran}
}
