Abstract
Cloud computing resources are offered in various forms, and surplus of computing resources are provided at cheaper price. A leading cloud computing vendor, Amazon Web Services, provides such opportunistic resources as EC2 spot instance whose price changes dynamically based on the resource demand from users. We analyze the spot instance price logs and apply various predictive analysis algorithms to better predict future spot instance price. By applying various train dataset modeling heuristics, we uncover that the SARIMA algorithm achieves the best prediction accuracy in spot price prediction; it shows 17% more accuracy than other algorithms that are widely used for spot instance applications. By applying contributions in this paper, we expect that spot instance users can decrease monetary cost while improving system stability.
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Acknowledgements
This work is supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP) (No. NRF-2015R1A5A7037615 and NRF-2016R1C1B2015135), the ICT R&D program of IITP (2017-0-00396), and the AWS Cloud Credits for Research program.
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Alkharif, S., Lee, K., Kim, H. (2018). Time-Series Analysis for Price Prediction of Opportunistic Cloud Computing Resources. In: Lee, W., Choi, W., Jung, S., Song, M. (eds) Proceedings of the 7th International Conference on Emerging Databases. Lecture Notes in Electrical Engineering, vol 461. Springer, Singapore. https://doi.org/10.1007/978-981-10-6520-0_23
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DOI: https://doi.org/10.1007/978-981-10-6520-0_23
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