Cloud federation enables service providers to collaborate to provide better services to customers. For cloud storage services, optimizing customer object placement for a member of a federation is a real challenge. Storage, migration, and latency costs need to be considered. These costs are contradictory in some cases. In this article, we modeled object placement as a multi-objective optimization problem. The proposed model takes into account parameters related to the local infrastructure, the federated environment, customer workloads, and their SLAs. For resolving this problem, we propose CDP-NSGAII IR , a Constraint Data Placement matheuristic based on NSGAII with Injection and Repair functions. The injection function aims to enhance the solutions’ quality. It consists to calculate some solutions using an exact method then inject them into the initial population of NSGAII. The repair function ensures that the solutions obey the problem constraints and so prevents from exploring large sets of unfeasible solutions. It reduces drastically the execution time of NSGAII. Experimental results show that the injection function improves the HV of NSGAII and the exact method by up to 94% and 60%, respectively, while the repair function reduces the execution time by an average of 68%.

A cloud federation gives to cloud service providers (CSP) the opportunity to collaborate in order to offer a better QoS to customers at a lower cost. To do so, CSPs make some spare resources available to others at a reduced cost. One of the most critical resources is the storage system as it represents the main system bottleneck. From this point of view, how to efficiently place data in a federation of Clouds with heterogeneous storage systems is a real challenge. To address this issue, one needs to accurately estimate the data placement cost. In this paper, we propose a cost model for hybrid storage systems in a cloud federation for a Database as a Service (DBaaS) application. It takes into account the storage system characteristics, customers I/O workloads and SLA. The proposed cost model considers both 1) Internal customers data placement cost including local placement, outsourcing, back-migration and penalty costs, and 2) External customers data placement cost including insourcing and geo-migration costs. It can be used to help in the decision-making process which aims to enhance customers QoS and reduce CSPs costs in a federation. Simulation results showed the relevance of the considered costs. We have shown that mis-considering some sub-costs may lead to a 95% cost error for external customers data placement and 80% for outsourcing customers. This may cause significant financial loss.

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