Dynamic Resource Allocation for Multi-Access Edge Computing in Urban Rail Transit

The development of information technology and intelligence in urban rail transit has attracted great attention. The requirement for compute-intensive and delay-sensitive services is increasing rapidly. As a new network paradigm, Multi-access Edge Computing (MEC) can offer an environment for information technology service and extend cloud computing capability to the edge of mobile communication network. So communication data can be timely processed near the source to effectively solve the limitation of on-board resources. In this paper, we consider the heterogeneous characteristics of services and different requirements of tasks in urban rail transit system, and a MEC system architecture based on task classification is designed. To effectively utilize communication and computing resources, a resource allocation strategy based on Task Classification Twin Delayed Deep Deterministic policy gradient (TC-TD3) algorithm is proposed, which includes communication and computing resources allocation. In addition, to address the offloading selection issue of multi-MEC servers, a task offloading algorithm based on balancing tasks priority of MEC is designed. In the system model, considering the random arrival time of tasks and the dynamic resource allocation at each time slot, we propose a dynamic task execution model which is more suitable for the urban rail transit system. The findings of the simulation demonstrate that the proposed scheme can significantly improve the task completion rate and reduce the task processing delay of different tasks.