Abstract
In this world of big data, hosting storage and analytics as cloud service is extremely relevant. In multi-user environments, there are chances for load imbalance during data placement. MapReduce like frameworks move computation towards data. However, because of load imbalance, some nodes cannot start computation on the node on which data is stored and may be compelled to start computation on some other nodes. This results in deteriorating data locality. In this case, data have to be copied to the computing node. This data transfer increases the job completion time. This paper proposes a data placement policy for clouds in which the data and virtual machines are collocated in the same set of physical servers. The physical servers in the cloud are grouped into partitions created using the minimum spanning tree. Experimental results show that this proposal improves node utilisation and reduces execution time over default placement in the cloud environment.
Published Version
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