Due to the problems exist in non-geographic federated clouds, the geographic ones are considered. Nevertheless, the approaches that have already been proposed to allocate resources across the geographical federated clouds have two basic problems that we will address in this article: (1) Lack of proper distribution of user requests leading to increases file transfer volume and cost, as well as response time to user requests, (2) Lack of appropriate resource sharing among requests due to: (1) the use of a centralized DC and (2) considering the satisfaction of single objective which case (1) suffers the problem of single-point of failure and case (2) raises an obstacle for the situations need considering multi conflicting objectives.

Concerning the problem of one, it should be said that as federal DCs are distributed globally in the geographic clouds, the cost of file transfer between DCs in these clouds is more focused than the concentrated ones. Since there has been no work in this field in the geo-distributed federated clouds, we have presented a new scheduling mechanism based on hypervolume for the distribution of applications that leads to increasing service quality and reducing file transfer cost.

Concerning the problem of two, the previous solutions in the geographic federated clouds have focused on a centralized resource sharing with single objective (increase of the cloud service provider (CSP) profit). These solutions not only just consider the CSP profitability, but, because of the possibility of failure of central broker of resource-sharing, suffer the single-point of failure. In this paper, we propose a new, autonomic and peer-to-peer multi-objective resource sharing approach that considers objectives: (1) enhancing the CSP's profit, (2) decreasing the network latency and (3) decreasing file transfer traffic and (3) increasing fairness in CSPs' profit.

The techniques presented in this paper are evaluated by extensive experiments using real workloads. To validate the proposed method, we have extended the CloudSim tool. The results of our experiments show the increase of performance in the scheduling and resource-sharing objectives among which the main objectives of average rate of success, profit and execution time were enhanced 8.5%, 15.47% and 25.84%, respectively compared with previous studies.

由于非地理联合云中存在问题，因此考虑了地理联合云。但是，已经提出的跨地理联合云分配资源的方法存在两个基本问题，我们将在本文中解决：（1）用户请求的适当分配不足会导致文件传输量和成本增加。作为对用户请求的响应时间，（2）由于以下原因，请求之间缺乏适当的资源共享：（1）使用集中式DC；（2）考虑满足单个目标，在这种情况下（1）会遇到以下问题：故障点和案例（2）为需要考虑多个冲突目标的情况带来了障碍。

关于“一”的问题，应该说，由于联邦DC在全球分布在地理云中，因此这些DC中DC之间的文件传输成本比集中的云更集中。由于在地理分布的联合云中该领域没有任何工作，我们提出了一种基于超卷的新调度机制来分发应用程序，从而提高了服务质量并降低了文件传输成本。

关于两个问题，地理联合云中的先前解决方案集中于具有单一目标（增加云服务提供商（CSP）利润）的集中式资源共享。这些解决方案不仅考虑了CSP的盈利能力，而且由于资源共享中央经纪人发生故障的可能性而遭受了单点故障。在本文中，我们提出了一种新的，自主的，对等的多目标资源共享方法，该方法考虑了以下目标：（1）提高CSP的利润，（2）减少网络延迟和（3）减少文件传输流量以及（3）增加CSP利润的公平性。

本文中介绍的技术通过使用实际工作负载的大量实验进行了评估。为了验证所提出的方法，我们扩展了CloudSim工具。我们的实验结果表明，在调度和资源共享目标方面的性能有所提高，其中平均成功率，利润和执行时间的主要目标与以前的研究相比分别提高了8.5％，15.47％和25.84％。