부산대학교 도서관

Mobile-edge Computing (MEC) has now emerged as a complement to cloud computing, providing computational capacity for the resources-constrained edge devices. Recently, intelligent computation offloading and cache placement stands as effective approaches to enhance the performance of dynamic MEC networks. In this paper, we propose an online centralized joint resource management approach, named Transformer-based Actor-Critic (TAC), to minimize the task execution time subject to resource constraints. We decouple this mixed-integer non-linear programming (MINLP) problem into a non-convex offloading decision part and a convex joint resources allocation part, and propose the TAC approach to address the non-convex task offloading problem with low computational complexity. In the joint resources management problem, the high-dimensional state-action space is addressed by the transformer-based actor-critic architecture. Through the proposed TAC, the joint cache, communication and computation resource management can be obtained without the knowledge of future task arrivals. Simulation results demonstrate that the TAC can save 48.4% average task execution time with only 2.3% additional computation delay compared to Random with lowest computational complexity. In particular, it further demonstrates great generalization ability to enhance the performance in untrained scenarios.