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Curtailing job completion time in MapReduce clouds through improved Virtual Machine allocation
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DOI：10.1016/j.compeleceng.2016.10.009.png)
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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. (C) 2016 Elsevier Ltd. All rights reserved.
Keyword:
MapReduce
Cloud computing
Big data
Virtual machines
Virtual machine allocation
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