Open AccessPredicting Virtual Machine Resource Consumption Based on Optimized Grey Model
Author(s) -
Aiwu Shi,
Di Gao,
Kai He
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/563/5/052002
Subject(s) - cloud computing , computer science , sorting , virtual machine , resource (disambiguation) , stability (learning theory) , resource consumption , resource allocation , scheme (mathematics) , enhanced data rates for gsm evolution , value (mathematics) , distributed computing , algorithm , artificial intelligence , machine learning , operating system , computer network , mathematics , ecology , mathematical analysis , biology
In order to relatively change the computing specifications of virtual machines (VMs for short) for the changing resource consumption of VMs, and reduce the impact of the changes, this paper proposes the optimized grey GM(1, 1) model to predict the resource consumption of VMs. Because VM resources may fluctuate greatly in a short time, the initial data are smoothed first, and then modeled, the model edge value is optimized according to the characteristics of cloud environment. Experiments show that the accuracy and stability of the prediction model can be improved by sorting out the initial data and optimizing the model. The prediction value is helpful to design the allocation scheme of VM resources, improve the utilization rate of physical resources in cloud environment.