Publication: DQN dynamic pricing and revenue driven service federation strategy
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2021-12
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IEEE
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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.
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Keywords
Federation, Pricing, Reinforcement learning, Optimization
Bibliographic citation
Martin-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.