Cloud service provisioning strategy using data center consortium clustering

Abstract

The rapid growth of data centers provides cloud users with better choices and services. An increasing number of companies are involved in the construction of data centers; at the same time, the original cloud service providers are constantly expanding new data centers to meet the needs of users. Therefore, the joint scheduling of multiple data centers has become a difficult problem that cloud service providers must face. Joint scheduling refers to the resource allocation among multiple data centers of a cloud service provider. That is, how can the virtual machine application of cloud users be deployed on the physical machine in multiple data centers. A single data center provisioning strategy can solve the scheduling problem within the data center. However, multiple data centers can make the previous provisioning strategy invalid due to geographical location and other differences. In this paper, the characteristics of different data centers are analyzed and clustered to form a data center consortium. Then, the virtual machine (VM) is classified according to the deadline. As cloud users purchase virtual machine resources from cloud service providers, they are billed by time in most cases. The deadline of the virtual machine refers to the time when the virtual machine ends its service. Then, we analyze the power consumption of the data center and deploy VMs based on the rule of avoiding hotspot deployment of VMs with priority energy consumption. Finally, a cloud service provisioning strategy using data center consortium clustering (PSDC3) is proposed and verified. It can be seen from the results that the algorithm can significantly reduce the number of hotspots and energy consumption of cloud data centers.