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• W3204388002 abstract "This paper proposes a dynamic pricing and revenue-driven service federation strategy based on a Deep Q-Network (DQN) to instantly and automatically decide federation across different service provider domains, each introduces dynamic service prices offering to its customers and towards other domains. A dynamic pricing model is considered in this work based on the analysis of real pricing data collected from public cloud provider, and upon this a dynamic arrival process as a result of the price changes is proposed for formulating the service federation problem as a Markov Decision Problem (MDP). In this work, several reinforcement learning algorithms are developed to solve the problem, and the presented results show that the DQN method reached 90% of the optimal revenue and outperformed existing state-of-the-art strategies, and it can learn the federation pricing dynamics to make optimum federation decisions according to price changes." @default.
• W3204388002 created "2021-10-11" @default.
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• W3204388002 date "2021-12-01" @default.
• W3204388002 modified "2023-10-18" @default.
• W3204388002 title "DQN Dynamic Pricing and Revenue Driven Service Federation Strategy" @default.
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• W3204388002 doi "https://doi.org/10.1109/tnsm.2021.3117589" @default.
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