Forecasting-based elastic cloud service research in an IaaS cloud

To deal with the issue that the delay time made by deploying virtual machine under the IaaS environment which provides virtual machine service for users leads low efficiency of elastic cloud service, an approach based on ARIMA model for predicting workload and forecasting the quantity of virtual machine in the future was proposed to settle the issue which has been leaded by time delay. Our method exploited the relationship between workload and the configuration of virtual machine resources, it predicted the workload firstly, and then estimated the quantity requirement of virtual machine. It improved efficiency of elastic cloud through reserving suitable virtual machine resource. Our research related to the private IaaS cloud environment which supplies virtual machine for users. The result of experiment and algorithm analysis revealed our method was accurate and practicable for decision of virtual machine.