Martín Pérez, JorgeAntevski, KirilGarcia Saavedra, AndresLi, XiBernardos Cano, Carlos Jesús2022-01-262022-01-262021-12Martin-Perez, J., Antevski, K., Garcia-Saavedra, A., Li, X. & Bernardos, C. J. (2021). DQN Dynamic Pricing and Revenue Driven Service Federation Strategy. IEEE Transactions on Network and Service Management, 18(4), 3987–4001.1932-4537https://hdl.handle.net/10016/33964This 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.15eng© 2021, IEEE.FederationPricingReinforcement learningOptimizationresearch articleTelecomunicacioneshttps://doi.org/10.1109/TNSM.2021.3117589open access398744001IEEE Transactions on Network and Service Management18AR/0000029010