DQN dynamic pricing and revenue driven service federation strategy Articles uri icon

publication date

  • December 2021

start page

  • 3987

end page

  • 4001


  • 4


  • 18

International Standard Serial Number (ISSN)

  • 1932-4537


  • 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.


  • Telecommunications


  • federation; pricing; reinforcement learning; optimization