Computation Resource Optimization for Large-Scale Intelligent Urban Rail Transit: A Mean-Field Game Approach

Abstract

Offloading tasks in smart devices (SDs) to an edge intelligence-empowered service centre (EISC) is a promising solution to support burgeoning intelligent applications in large-scale intelligent urban rail transits (URTs). However, dynamic computation resource allocation is still a crucial challenge, facing that various large-scale SDs share the EISC computation resources and the reality that the allocated computation resource for an SD is coupled with the offloading rate of all SDs. This paper proposes a joint dynamic offloading rate control and computation resource optimization method for large-scale intelligent URTs. Firstly, we model the large-scale multi-agent computation resource competition problem by a multi-player differential game (MPDG) and prove that the Nash equilibrium (NE) based optimal solution exists for each SDs. Then, we transform the MPDG model into a mean-field game (MFG). By introducing the mean-field into the game, we can solve the multi-agent optimization problem with a single-agent optimization method. We illustrate the rationality of the MFG model and propose an iterative solution method based on the finite difference method to derive the solution. Finally, we propose a z-transforming-based control method to dynamically reschedule computation resources among intelligent applications to achieve a satisfactory quality of service (QoS). Extensive simulation results show that our proposed scheme can significantly improve the performance of intelligent URTs.