A Cybertwin-Based Trading Mechanism for Personalized Transmission Services in Cloud Native Networks

Abstract

The Cybertwin-based cloud native network (CCNN) with a cloud-edge-end architecture is to provide personalized user services, which includes both wireless access networks and cloud networks. However, the limited availability of resources and intense user competition present significant challenges. While prior studies have contributed to this issue, they overlook the feasibility of using resources as the trading object in a user-centric approach, particularly in wireless networks. To address this gap, this letter proposes a Cybertwin-based Personalized Transmission Service Trading Mechanism (CPTSTM), employing a one-to-many concurrent bilateral negotiation model for dynamic multi-provider trading. An optimization problem is formulated to minimize user costs by modeling the negotiation as a sequential decision-making process, accounting for individual rationality and system uncertainties. Given restricted and hybrid action spaces, we introduce a Constrained Parameterized Deep Q-learning Network (CPDQN)-based negotiation strategy integrating offer evaluation, acceptance, and bidding strategies. Simulation results confirm the feasibility of the proposed mechanism and demonstrate that the CPDQN-based algorithm outperforms existing methods in terms of trade success rate (nearly 100%) and the number of negotiation rounds (reduced by 75%) while maintaining comparable user utility across various scenarios.