Cloud Resource Demand Prediction using Machine Learning in the Context of QoS Parameters

Abstract

Predicting demand for computing resources in any system is a vital task since it allows the optimized management of resources. To some degree, cloud computing reduces the urgency of accurate prediction as resources can be scaled on demand, which may, however, result in excessive costs. Numerous methods of optimizing cloud computing resources have been proposed, but such optimization commonly degrades system responsiveness which results in quality of service deterioration. This paper presents a novel approach, using anomaly detection and machine learning to achieve cost-optimized and QoS-constrained cloud resource configuration. The utilization of these techniques enables our solution to adapt to different system characteristics and different QoS constraints. Our solution was evaluated using a system located in Microsoft’s Azure cloud environment, and its efficiency in other providers’ computing clouds was estimated as well. Experiment results demonstrate a cost reduction ranging from 51% to 85% (for PaaS/IaaS) over the tested period.

Data Availability

The data that support the findings of this study are available from OTI Europa ASEC but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of OTI Europa ASEC.

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Acknowledgements

The research presented in this paper was supported by funds from the Polish Ministry of Science and Higher Education assigned to the AGH University of Science and Technology.

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Correspondence to Piotr Nawrocki.

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Nawrocki, P., Osypanka, P. Cloud Resource Demand Prediction using Machine Learning in the Context of QoS Parameters. J Grid Computing 19, 20 (2021). https://doi.org/10.1007/s10723-021-09561-3

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Keywords

  • Cloud computing
  • Resource usage prediction
  • Anomaly detection
  • Machine learning
  • Quality of service
  • Resource cost optimization