A profit-effective function service pricing approach for serverless edge computing function offloading

In recent years, edge computing services have continued to develop and have been better integrated with serverless computing, leading to the improvement of the performance and concurrent request handling capabilities of edge servers. Therefore, an increasing number of IoT devices are willing to pay a certain amount of service processing fees to offload some computing tasks to edge servers for execution, with the aim of meeting their latency requirements. However, the computing capacity and storage space of edge servers at a single base station are still limited. Therefore, base stations must decide which task images to cache for future execution and price these computing services to control the computing offloading of IoT devices, so as to maximize their expected profit under the constraints of limited computing capacity and memory space. In this paper, we stand from the perspective of base stations and formulate the caching and pricing of function images at a base station, as well as the function offloading process of IoT devices, as a Markov Decision Process (MDP). We adopt a Proximal Policy Optimization (PPO)-based function service pricing adjustment algorithm to optimize the profit of base stations. Finally, we evaluate our approach through simulation experiments and compare it with baseline methods. The results show that our approach can significantly improve base stations’ expected profit in various scenarios.