# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 7 Conclusions  
In this paper, we examined the potential of machine-learning techniques for predicting future prices for IaaS services. ML price-prediction models were built for Amazon's instance families using the CatBoost algorithm. Amazon is the leading provider in the IaaS market, while RI is the most mature pricing option; thus, we focused on it as a first step in our exploration. The models' close correlation with actual prices showed that they were capable of capturing and interpreting the fundamental pricing trends and patterns. Evaluation results show that shorter time periods provided lower MAE values, suggesting more accuracy in projecting cloud service pricing. This means that current data, independent of instance family, has a greater effect on pricing patterns and market trends in the cloud services business. In addition, the analysis of ML algorithm performance over different instance families demonstrated that a disruptive change in pricing policy, not related to a corresponding change in instance properties, as in the case of memory-optimized instances, may be predicted as a trend, though accurate results are difficult to obtain. This could only be achieved if related external factors, as resource cost, could be provided as input parameters into the model. This study focuses on the task of forecasting the complex and constantly shifting pricing structures of cloud services. Our research stands out due to its broad examination of various offers within each instance family, distinguishing it from previous studies that primarily concentrated on specific instance types in the spot instance market. The all-encompassing nature of our methodology enables an expanded understanding of the pricing dynamics among various instance families, offering valuable insights for cloud consumers who employ a wide range of services. It is essential to recognize the limitations of this research. The precision of the forecasts is highly dependent on the availability and quality of historical data. Incomplete or skewed data can induce uncertainty and have an effect on the efficacy of the models. In addition, the models presume that the factors influencing cloud pricing will remain relatively stable over time; however, external factors such as market dynamics or changes in the competitive landscape may impact the accuracy of the predictions. To further improve the precision and generalizability of the models, future research may investigate multiple paths. First, the incorporation of more diverse datasets from multiple sources would broaden the perspective and enhance the performance of the models. In addition, investigating sophisticated machine-learning algorithms and techniques, such as ensemble methods or deep learning, could result in further enhancements to the accuracy of predictions. In addition, considering the wide variety of pricing patterns observed in certain instance families, a clustering strategy could be investigated. By organizing instances according to their specific characteristics and pricing dynamics, models can account for the heterogeneity within families, resulting in more accurate predictions. This study contributes to the expanding corpus of knowledge concerning machine-learning applications in cloud computing pricing. The developed models provide businesses with valuable insights for optimizing cloud resource utilization, making informed decisions, and managing IT budgets effectively. Recognizing the limitations, the findings pave the way for future research and the refinement of predictive models in cloud computing pricing, which will ultimately benefit organizations attempting to navigate the dynamic cloud services landscape. Conceptualization, M.N. and G.F.; methodology, G.F.; software, G.F.; validation, C.M., T.K. and M.N. ; formal analysis, G.F., M.N. and E.F.; investigation, E.F.; resources, E.F.; data curation, G.F.; writing--original draft preparation, G.F.; writing--review and editing, T.K., C.M. and M.N. ; visualization, G.F.; supervision, C.M. ; project administration, M.N.