A Dynamic Pricing Strategy for Load Balancing Across Multiple Edge Servers

Bing Shi, Feiyang Chen

Abstract: As a new computing paradigm, mobile edge computing can meet users’ computing demands with low latency. In reality, multiple edge servers with different computing capabilities are usually deployed in a distributed manner, which means that computing tasks cannot be offloaded by a centralized manager. In such a situation, how to achieve load balancing among multiple edge servers is a challenging problem. In the edge computing, edge servers usually set prices for the provided service, which will affect users’ offloading costs and thus can have impacts on users’ offloading decisions. Therefore, in this paper, we design a dynamic pricing strategy for edge servers based on multi-agent reinforcement learning, which can affect users’ offloading costs and thus motivate users to make reasonable offloading decisions in order to improve the load balancing of the entire system. Furthermore, we run extensive experiments to evaluate our dynamic pricing strategy against four benchmark strategies. The experimental results show that our strategy can effectively improve the long-term load balancing in different user-demanding environments.