RT Journal Article
T1 DQN dynamic pricing and revenue driven service federation strategy
A1 Martín Pérez, Jorge
A1 Antevski, Kiril
A1 Garcia Saavedra, Andres
A1 Li, Xi
A1 Bernardos Cano, Carlos Jesús
AB 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.
PB IEEE
SN 1932-4537
YR 2021
FD 2021-12
LK https://hdl.handle.net/10016/33964
UL https://hdl.handle.net/10016/33964
LA eng
DS e-Archivo
RD 27 jul. 2024