Virtual Machine Instance’s Price Prediction using Machinelearning Techniques at the Cloud Data Center

Virtual Machine (VM) instance price prediction in cloud computing is an emerging and important research area. VM instance’s price prediction is used for different purposes such as reducing energy consumption, maintaining Service Level Agreement (SLA), and balancing workload at cloud data centers. In this paper, we propose a Seasonal Auto-Regressive Moving Average (SARIMA) based VM instance price prediction. We also investigate two VM instance price prediction models known as Auto Regressive Integrated Moving Average (ARIMA), and Long ShortTerm Memory (LSTM). The experimental results show that the proposed SARIMA (0,1,0) (1,1,0) instance’s price prediction model outperforms the ARIMA and LSTM models with a MAPE percentage of 1.147.