Geo-Distributed BigData Processing for Maximizing Profit in Federated Clouds Environment

Managing and processing BigData in geo-distributed datacenters gain much attention in recent years. Despite the increasing attention on this topic, most efforts have been focused on user-centric solutions, and unfortunately much less on the difficulties encountered by Cloud providers to improve their profits. Highly efficient framework for geo-distributed BigData processing in cloud federation environment is a crucial solution to maximize profit of the cloud providers. The objective of this paper is to maximize the profit for cloud providers by minimizing costs and penalty. This work proposes to transfer compute (computations) to geo-distributed data and outsourcing only the desired data to idles resources of federated clouds in order to minimize job costs; and proposes a jobs reordering dynamic approach to minimize the penalties costs. The performance evaluation proves that our proposed algorithm can maximize profit, reduce the MapReduce jobs costs and improve utilization of clusters resources.