# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 2 Related Work
### Aim and Contribution of This Work  
Based on the aforementioned results, we were encouraged to explore the application of ML methods to predict the evolution of standard product prices offered by the provider, as, for example, a reserved instance, on a longer-term basis. Such products are offered on a fixed price by the provider, with no biting allowed. In this case, prediction on an hourly or daily basis as in spot instance is not useful. Prediction should scale on a monthly or yearly basis to provide meaningful information to end-users.  
For this effort, we decided to utilize the CatBoost algorithm. CatBoost is a popular algorithm applied in several fields such as electricity [27], financial markets [28], and healthcare [29]. Regarding the cloud industry, CatBoost was already used to explore cloud security on a dataset based on reserved instance features [30].  
The CatBoost algorithm works with a broad variety of data types and addresses a wide range of data challenges. In addition, CatBoost can support numerical and categorical variables and has an effective method for dealing with categorical data. Finally, CatBoost is a robust machine-learning algorithm that serves efficiently for forecasting cases. As a result, it is seen to be a suitable fit for our suggested ML technique [31].  
One should have in mind that the accuracy of the explored ML algorithms utilized for spot instance price prediction varies significantly, based on the evidence of existing work. Thus, the selection of one of the explored or proposed algorithms was not a straightforward solution in our case. Furthermore, the prediction period and the sample variance in our case was much different than that in the cases of the spot instance price prediction studied in the literature.  
In addition, we focus on how the selection of time periods and corresponding datasets can improve the accuracy of the ML price prediction. Based on existing work [10], longer time periods do not necessarily improve accuracy. We wanted to explore this assumption in our case.  
Furthermore, we wanted to explore whether significant changes in the pricing policies may be captivated by such a model. As a first step, we used a specific ML algorithm, to better understand the dynamics and limitations of historical data in price prediction, as in many cases price increase or decrease is a business decision, related to the introduction of a new product or service region, not directly related to the product characteristics. As the results are encouraging, the exploration of alternative ML algorithms should also be considered.  
Concluding the current section, it is evident that cloud price prediction has been discussed in several papers. However, to the best of our knowledge, it is the first attempt to predict RI prices using machine learning in contrast to spot instances. The current paper can be considered the initial phase of this exploration since the proposed models based on CatBoost are trained on datasets from diverse time periods, in order to achieve an acceptable predicted accuracy. Finally, a comparison of actual and predicted prices is shown, underscoring the models' dependability as well as potential challenges.