Abstract
Modern big data analysis and business intelligence applications come with high resource demands, along with a requirement for large data transfer between storage and compute nodes. Since the available resources of a single data center might not be sufficient to host these applications, federated cloud systems present a promising solution. The objective of each cloud service provider in a federation is to maximize its own profit and to minimize its total operating cost. To achieve these objectives, spatial variation in the energy cost and bandwidth cost could be leveraged while allocating the workload. We propose a hierarchical approach for resource management in a federated cloud, catering to the requirements of each provider in the federation. We formulate the placement of a data-intensive applications as an optimization problem to minimize the total operating cost, including the energy and communication costs. We propose an algorithm to allocate the virtual components of a data-intensive application, in two phases; partitioning and mapping. While partitioning creates clusters of correlated nodes, mapping allocates the created clusters to data centers that minimize the total cost. Through extensive experiments in different scenarios, we demonstrate that the proposed algorithm achieves a significant reduction in the total operating cost.