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.

通过学习为基础的资源配置方法，实现了云计算中的低成本、高效能需求的资源调配方案。通过综合评估，我们的方法有效地满足了大多数需求，并且受到了性能目标的支持。