Dynamic Effective Resource Allocation Based on Cloud Computing Learning Model

In cloud computing field, a service-provider offers large number of resources for customers with a relatively low cost. However, any resource allocation model has to consider computational resources as well as network resources to accurately reflect practical demands. In order to rationally allocate the limited resources to users and improve resource utilization and energy efficiency of cloud systems as much as possible, a dynamic effective resource allocation algorithm based on cloud computing learning model is proposed. According to the dynamic effective resources allocation, Quality of Service Standards Framework is adopted to obtain cloud service management mechanism. In addition, the resources auction strategy and dynamic bilateral game strategy are also adopted to effectively leverage the interest-relationship between the user and the cloud provider so as to assign resources to these users with bigger demand. In the computation of cloud system and resources allocation of storage tasks, the cloud-based learning model is used to balance the demand of energy and resources so as to achieve optimal system efficiency. Simulation experiment results show that our proposed algorithm can assign more reasonably resource and energy for system computation and storage tasks and the performance of the algorithm is superior to other comparison algorithms on the utilization of resource and energy