Collaborative Service Caching, Task Offloading, and Resource Allocation in Caching-Assisted Mobile Edge Computing

Mobile Edge Computing (MEC) revolutionizes the traditional cloud-based computing paradigm by moving resources in proximity to the network edge, aiming to cater to the rigorous requirements of emerging latency-sensitive applications. However, the escalating resource demands intensify the competition among user devices (UDs). Thus, it is essential to coordinate task offloading and resource scheduling while ensuring fairness among users in MEC. Despite the crucial role of user fairness in motivating task offloading in MEC, it is often overlooked in existing literature. Therefore, we in this paper propose a caching-enhanced MEC framework and formulate a collaborative service caching, task offloading, and multi-resource allocation problem to maximize average user satisfaction. Multiple factors contribute to the difficulty in solving the optimization problem, including constrained resource capabilities, user mobility, service heterogeneity, and spatial demand coupling. Consequently, we transform the origin problem into two distinct subproblems – the service caching and task offloading problem, and the multi-resource allocation problem, respectively. Then, the Advantage Actor-Critic (A2C) based approach is proposed to address the former problem, while a Lagrangian duality-based approach is adopted to tackle the latter problem. The simulation results demonstrate the superior performance of the proposed solution in comparison to several baseline methods.