Abstract
Recently, with the rise in demand for reliable and economical cloud services, there is a rise in the number of cloud providers competing among each other. In such a competitive open market of multiple cloud providers, providers aim to model the selling prices of their requested resources in real-time to maximise their revenue. In this regard, there is a pressing need for an efficient real-time pricing mechanism, that effectively considers a change in the supply and demand of the resources in a certain open cloud market. In this research, we propose a reinforcement learning-based real-time pricing mechanism for dynamically modelling the prices of the requested resources. In specific, the proposed real-time pricing mechanism in a reverse-auction based resource allocation paradigm, which utilises the supply/demand of the resources and undisclosed preferences of the cloud users. Further, we compare the proposed approach with two state-of-the-art resource allocation approaches and the proposed approach outperforms the other two resource allocation approaches.
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Mishra, P., Moustafa, A., Ito, T. (2020). Reinforcement Learning Based Real-Time Pricing in Open Cloud Markets. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_37
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DOI: https://doi.org/10.1007/978-3-030-55789-8_37
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