Analysis And Prediction Of Resource Usage In The Cloud Based On Improved Random Forest Model

Cloud computing provides users with high flexibility.It also provides cloud providers with flexible and easy resource management.However,most cloud facilities have very low resource utilization with high operating costs and high energy consumption.The key to make right management decisions in big data center is predicting the future load of hosts and tasks in advance.However,the dynamics,uncertainty and mutability of tasks in cloud computing make the prediction difficult.How to improve the prediction performance of loads in the cloud is one of the most important problems tackled in the effective management of cloud resources.In order to solve the above problems,the following work is done in this paper:(1)Resource usage of several main clouds is analyzed in detail,and characteristics of resource usage are extracted too.(2)Performance evaluation function of task resource utilization prediction is also designed according to the characteristics of tasks in cloud platform.(3)A processor and memory utilization prediction method for tasks in cloud platform is designed and implemented.Then we test this method on Google Trace.The result turns out our proposed model's PEFOT is reduced by an average of 3.112%compared with proposed method.(4)Performance evaluation function of host resource utilization level prediction is designed according to the characteristics of hosts cloud platform..(5)Upsampling and downsampling methods are used to solve the serious imbalance of host resource usage level on cloud platform.(6)A processor and memory utilization prediction method for host in cloud platform is is designed and implemented.Then we conduct experiments using this method on Google Trace.The result shows that our proposed model's PEFOH improves 17.5% on average compared with proposed method.