Online Task Offloading and Resource Allocation in Two-Tier Mobile-Edge Computing Network

This work focuses on the tasks offloading and resource allocation based on cloud-edge computing. We consider a two-tier multi-user mobile-edge computing (MEC) network, where wireless devices can offload the task data to edge servers or the cloud center. Our goal is to find optimal offloading and resource allocation decisions to maximize the average computation rate while reducing the average cost of edge and cloud services, subject to the stability of the task queue and average power limit. First, the problem is transformed into a deterministic problem for each time frame utilizing the Lyapunov optimization theory. Then, an algorithm is proposed, combined with deep reinforcement learning (DRL), where DRL is used to make offloading decisions. This problem can be easily solved once offloading decisions are known. Finally, a numerical simulation is given to evaluate the algorithm's performance, which shows that our algorithm solves well and takes less time to compute.