ISBN: 978-981-11-0008-6 DOI: 10.18178/wcse.2016.06.138
The Energy-Consumption of CPU and Memory Prediction in Cloud Computing Based on GREY-ARIMA Model
Abstract— The energy-consumption of data center in cloud computing is a heated issues. This paper
focuses on the challenges of operation and power management in cloud platform, and presents a GMARIMA
model, which is simultaneously exploiting autoregressive integrated moving average(ARIMA model)
and GM(1,1) model. This combination is used for CPU and Memory in the cloud computing experiment to
model, test and forecast. The experiments revealed the GM-ARIMA model, in the minimum amount of
history record, is an efficient and feasible model for the limited sampled forecasting.
Index Terms— cloud computing, Grey-ARIMA model, the consumption of CPU and memory, prediction research.
Yong Shao, Yuxiang Zhang, Changshun Yan, Shengchang Wang
College of Software Engineering, Beijing University of Technology, CHINA
Cite: Yong Shao, Yuxiang Zhang, Changshun Yan, Shengchang Wang, "The Energy-Consumption of CPU and Memory Prediction in Cloud Computing Based on GREY-ARIMA Model," Proceedings of 2016 6th International Workshop on Computer Science and Engineering, pp. 773-776, Tokyo, 17-19 June, 2016.