Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS

When exploring alternative cloud solution designs, it is important to also consider cost. Thus, having a comprehensive view of the cloud market and future price evolution allows well-informed decisions to choose between alternatives. Cloud providers offer various service types with different pricing policies. Currently, infrastructure-as-a-Service (IaaS) is considered the most mature cloud service, while reserved instances, where virtual machines are reserved for a fixed period of time, have the largest market share. In this work, we employ a machine-learning approach based on the CatBoost algorithm to explore a price-prediction model for the reserve instance market. The analysis is based on historical data provided by AmazonWeb Services from 2016 to 2022. Early results demonstrate the machine-learning model's ability to capture the underlying evolution patterns and predict future trends. Findings suggest that prediction accuracy is not improved by integrating data from older time periods.

Automated summary

This study uses machine learning to predict the price model of reserved instances in the cloud service market based on historical data. Preliminary results show that the machine learning model accurately captures the evolution patterns and predicts trends.