Cost-aware Cloud Storage Service Allocation for Distributed Data Gathering

In today cyber-infrastructures, large datasets are produced in real-time by different sources geographically distributed. These data must be acquired and preserved for further use in knowledge extraction. In the context of multi-cloud environments, the cost-efficient storage service selection is a challenge. There are plenty of Cloud storage providers offering multiple options so, it is crucial to select the best solution in terms of cost and quality of service that meet customers requirements. Due to its multi-objective nature, the process of optimal service selection becomes a difficult problem. In this paper, we study the multi-objective optimization problem for storage service selection. We start from a real world case scenario and build our mathematical model for the optimization problem. Then we propose an aggregated linear programming technique to find a near optimal solution for the service selection problem.