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 GM-ARIMA 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.