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.

现代大数据分析和商业智能应用程序对资源的要求很高，同时还要求在存储和计算节点之间进行大数据传输。由于单个数据中心的可用资源可能不足以托管这些应用程序，因此联合云系统提出了一种有前途的解决方案。联盟中每个云服务提供商的目标是最大程度地提高自己的利润，并最大程度地降低其总运营成本。为了实现这些目标，可以在分配工作负荷时利用能源成本和带宽成本的空间变化。我们针对联盟云中的资源管理提出了一种分层方法，以满足联盟中每个提供商的要求。我们将数据密集型应用程序的放置公式化为优化问题，以最大程度地降低总运营成本（包括能源和通信成本）。我们提出了一种分两个阶段分配数据密集型应用程序的虚拟组件的算法：分区和映射。虽然分区创建相关节点的群集，映射分配所创建的簇到数据中心，最大限度地减少总成本。通过在不同情况下进行广泛的实验，我们证明了所提出的算法可显着降低总运营成本。