Joint Task Offloading and Resource Allocation via Proximal Policy Optimization for Mobile Edge Computing Network

Various innovative applications of emerging mobile Internet have exploded in recent years, which brings huge challenges to terminal devices with limited CPU computing ability and battery capacity. The realization of high-performance computing offloading based on different optimization indicators (e.g., task delay and energy consumption) is currently a research hotspot in the field of mobile edge computing (MEC). This paper proposes a joint task offloading and resource allocation algorithm via proximal policy optimization for multiple terminal users and multiple MEC servers. The proposed algorithm designs the local task buffer queues for terminal users and edge task buffer queues for MEC servers, which allows the tasks to be executed on buffer queues in a first-in-first-out way, leading to a precise calculation of waiting delays of tasks. Moreover, it formulates the objective optimization problem as the Markov decision process and employs the proximal policy optimization algorithm to minimize the weighted sum of the task delay and energy consumption. Simulation results show the proposed algorithm outperforms the baselines with better performance.