Dynamic Service Caching Aided Computation Offloading Optimization Algorithm for Mobile-Edge Networks

The widespread adoption of computation- and communication-intensive applications, such as object detection, VR/AR, and telemedicine, has significantly alleviated transmission pressure on backbone networks and improved user experience. However, efficiently managing and computing these tasks on user sides remains a significant challenge, particularly under resource-constrained conditions. To address this problem, we propose a new service caching decision method based on deep dueling double Q-network (D3QN) by employing a learnable policy to handle the unknown task requests and determine the optimal caching strategies. Additionally, the limited storage capacity of edge servers (ES) is mitigated by forwarding the resource-intensive or infrequently requested tasks to the cloud data centers (CDC). The channel selection problem is modeled as a multiuser game and a distributed method is developed to achieve the Nash Equilibrium (NE). Simulation results demonstrate that the proposed method outperforms the existing benchmarks, showcasing its effectiveness in managing complex, dynamic environments.