Efficient distribution of mapreduce jobs for maximizing profit on federated cloud.

Provisioning the MapReduce data-intensive applications across geo-distributed cloud federation is a key rationale behind the cost effectiveness and performance improvement. The objective of this paper is to maximize the profit for service providers by minimizing costs and penalty. This work proposes a fully distributed scheduling algorithm to process MapReduce data-intensive applications across geo-distributed clusters in federated clouds. The proposed algorithm takes advantage of data locality to reduce penalty while maximizing the profit. The performance evaluation proves that our proposed algorithm can maximize profit, reduce the MapReduce jobs costs and improve utilization of idle VMs of clusters.