Dynamic Pricing in Multi-Tenant MANO With Resource Sharing: A Stackelberg Game Approach

Abstract

Network slicing is used to support the stringent requirements of sixth generation (6G) services by dividing an infrastructure network into multiple logical networks that can enable service-oriented resource allocation. However, there are several orchestration issues when considering multiple infrastructure providers (InPs) and multiple tenants in a recursive architecture. There are also challenging issues in designing efficient auction mechanisms for such multi-domain and multi-tenant network slicing. To address these challenges, we consider multi-tenant management and orchestration as a multi-buyer, multi-seller scenario, and propose a novel two-stage auction mechanism that aims to increase the overall utility of all participants while mitigating the overall cost of the network. We formulate this two-stage auction mechanism as a multi-leader multi-follower (MLMF) Stackelberg game approach that converges to a Stackelberg equilibrium. In this game, there are multiple InPs that lease network, computing, and storage infrastructure resources to multiple Tier1 tenants in the first stage of the auction mechanism. Next, Tier1 tenants instantiate triple 6G slices as extremely reliable and low-latency communications (eURLLC), ultra-massive machine-type communications (umMTC), and further enhanced mobile broadband (FeMBB) slices, and lease smaller slices to Tier2 tenants through the second step of the auction mechanism. Tier2 tenants then serve different eURLLC, umMTC, and FeMBB users who have specific and mostly different requirements and constraints, while Tier2 tenants manage their own resources to maximize their utility. Due to the distributed nature of the proposed problem, we consider distributed reinforcement learning (DRL) as a solution. Simulation results show that our DRL-based solution increases the average profit of the network by 19% compared to the existing state-of-the-art benchmark.

Keywords

• Lease transfer
• infrastructure provider
• multi-domain network slicing
• MLMF Stackelberg game
• multi-timescale decision making
• distributed reinforcement learning