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

Abstract

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 Amazon Web 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.

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“…In Ref. [76], Fragiadakis et al's use of machine learning to forecast the costs of cloud services, particularly Amazon IaaS, shows how AI has the ability to lower costs and increase accessibility to cloud computing. In Ref.…”