Abstract
Predicting computing resource usage in any system allows optimized management of resources. As cloud computing is gaining popularity, the urgency of accurate prediction is reduced as resources can be scaled on demand. However, this may result in excessive costs, and therefore there is a considerable body of work devoted to cloud resource optimization which can significantly reduce the costs of cloud computing. The most promising methods employ load prediction and resource scaling based on forecast values. However, prediction quality depends on prediction method selection, as different load characteristics require different forecasting mechanisms. This paper presents a novel approach that incorporates data-driven adaptation of prediction algorithms to generate short- and long-term cloud resource usage predictions and enables the proposed solution to readjust to different load characteristics as well as both temporary and permanent usage changes. First, preliminary tests were performed that yielded promising results – up to 36% better prediction quality. Subsequently, a fully autonomous, multi-stage optimization solution was proposed. The proposed approach was evaluated using real-life historical data from various production servers. Experiment results demonstrate 9.28% to 80.68% better prediction quality when compared to static algorithm selection.
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Data Availability
The data that support the findings of this study are available from Polcom, but restrictions apply to the availability of these data, which were used under license for the current study, and thus are not publicly available. The data are, however, available from the authors upon reasonable request and with permission of Polcom.
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Acknowledgements
The research presented in this paper was supported by funds from the Polish Ministry of Education and Science allocated to the AGH University of Science and Technology. The authors would like to thank Polcom for providing the data used in the tests.
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The research presented in this paper was supported by funds from the Polish Ministry of Education and Science allocated to the AGH University of Science and Technology.
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Piotr Nawrocki – developed the concept of the article, designed the structure of the article, analyzed the available literature (Section 2), developed the introduction and summary, checked the entire article. Patryk Osypanka – developed Sections 4, 5, and summary. Beata Posluszny – developed Section 3 and partly Section 2.
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Nawrocki, P., Osypanka, P. & Posluszny, B. Data-Driven Adaptive Prediction of Cloud Resource Usage. J Grid Computing 21, 6 (2023). https://doi.org/10.1007/s10723-022-09641-y
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DOI: https://doi.org/10.1007/s10723-022-09641-y