Due to the recent wide use of computational resources in cloud computing, new resource provisioning challenges have been emerged. Resource provisioning techniques must keep total costs to a minimum while meeting the requirements of the requests. According to widely usage of cloud services, it seems more challenging to develop effective schemes for provisioning services cost-effectively; we have proposed a novel learning based resource provisioning approach that achieves cost-reduction guarantees of demands. The contributions of our optimized resource provisioning (ORP) approach are as follows. Firstly, it is designed to provide a cost-effective method to efficiently handle the provisioning of requested applications; while most of the existing models allow only workflows in general which cares about the dependencies of the tasks, ORP performs based on services of which applications comprised and cares about their efficient provisioning totally. Secondly, it is a learning automata-based approach which selects the most proper resources for hosting each service of the demanded application; our approach considers both cost and service requirements together for deploying applications. Thirdly, a comprehensive evaluation is performed for three typical workloads: data-intensive, process-intensive and normal applications. The experimental results show that our method adapts most of the requirements efficiently, and furthermore the resulting performance meets our design goals.

翻译：由于近期计算资源在云计算中的广泛使用，资源供应领域涌现出新的挑战。资源供应技术必须在满足请求需求的同时将总成本降至最低。鉴于云服务的普遍应用，开发具有成本效益的供应服务方案愈发具有挑战性；我们提出了一种新颖的基于学习的资源供应方法，能够实现需求成本降低的保障。我们的优化资源供应（ORP）方法的贡献如下：首先，该方法旨在提供一种成本效益高的方案来高效处理所请求应用的供应问题；而现有模型大多仅支持通用工作流且侧重于任务依赖关系，ORP则基于构成应用的各项服务进行运作，并全面关注这些服务的高效供应。其次，这是一种基于学习自动机的方法，可为所请求应用的每项服务选择最合适的资源主机；我们的方法在部署应用时同时考虑成本与服务需求。最后，针对三种典型工作负载——数据密集型、计算密集型及常规应用——进行了全面评估。实验结果表明，我们的方法能高效适配大多数需求，且最终性能达到了设计目标。