Task Offloading Scheme Based on Proximal Policy Optimization Algorithm

The rapid development of mobile Internet technology has made users’ requirements for quality of service (QoS) continuously improve. The task unloading process of mobile edge computing has the problem that it is impossible to balance delay and energy consumption for task unloading under the condition of fluctuating network bandwidth. To address this issue, this paper proposes a task offloading scheme based on the Proximal Policy Optimization (PPO) algorithm. On the basis of traditional cloud edge collaborative architecture, the collaborative computing mechanism between edge node devices is further integrated, and the concept of service caching is introduced to reduce duplicate data transmission, reduce communication latency and network load, and improve overall system performance. Firstly, this article constructs an energy efficiency function with a certain weight ratio of energy consumption and latency as the core optimization objective. Then, the task offloading process of mobile terminal devices is modeled as a Markov Decision Process (MDP). Finally, the deep reinforcement learning PPO algorithm is used for training and learning, and the model is solved. The simulation results show that the proposed scheme has significant advantages in reducing energy consumption and latency compared to the comparative scheme.