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

Author

Listed:
  • George Fragiadakis

    (Department of Informatics and Telematics, Harokopio University of Athens, 17671 Kallithea, Greece
    These authors contributed equally to this work.)

  • Evangelia Filiopoulou

    (Department of Informatics and Telematics, Harokopio University of Athens, 17671 Kallithea, Greece
    These authors contributed equally to this work.)

  • Christos Michalakelis

    (Department of Informatics and Telematics, Harokopio University of Athens, 17671 Kallithea, Greece
    These authors contributed equally to this work.)

  • Thomas Kamalakis

    (Department of Informatics and Telematics, Harokopio University of Athens, 17671 Kallithea, Greece
    These authors contributed equally to this work.)

  • Mara Nikolaidou

    (Department of Informatics and Telematics, Harokopio University of Athens, 17671 Kallithea, Greece
    These authors contributed equally to this work.)

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.

Suggested Citation

  • George Fragiadakis & Evangelia Filiopoulou & Christos Michalakelis & Thomas Kamalakis & Mara Nikolaidou, 2023. "Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS," Future Internet, MDPI, vol. 15(8), pages 1-19, August.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:8:p:277-:d:1220686
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    References listed on IDEAS

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    1. Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
    2. Tao Hong & Pierre Pinson & Yi Wang & Rafal Weron & Dazhi Yang & Hamidreza Zareipour, 2020. "Energy forecasting: A review and outlook," WORking papers in Management Science (WORMS) WORMS/20/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    3. Se-Hak Chun, 2019. "Cloud Services and Pricing Strategies for Sustainable Business Models: Analytical and Numerical Approaches," Sustainability, MDPI, vol. 12(1), pages 1-15, December.
    4. Fan Zhang & Hasan Fleyeh & Chris Bales, 2022. "A hybrid model based on bidirectional long short-term memory neural network and Catboost for short-term electricity spot price forecasting," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(2), pages 301-325, March.
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    Cited by:

    1. Jerry Chou & Wu-Chun Chung, 2024. "Cloud Computing and High Performance Computing (HPC) Advances for Next Generation Internet," Future Internet, MDPI, vol. 16(12), pages 1-4, December.

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