Real-Time Dynamic Pricing for Edge Computing Services: A Market Perspective

• Published in: IEEE access Vol. 12; pp. 134754 - 134769
• Main Authors: Park, Sangdon, Bae, Sohee, Lee, Joohyung, Sung, Youngchul
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biological system modeling; CAPEX; Capital expenditures; Closed form solutions; Cloud computing; Computational modeling; Cost analysis; dynamic pricing; Economic models; Edge computing; Edge computing systems; Expenditures; market analysis; OPEX; Optimization; Pricing; Pricing policies; Profitability; Real time operation; Resource allocation; Resource management; Resource utilization; Servers; Simulation models; Static models; Supply & demand
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3462499

Edge computing has emerged as a crucial technology for addressing the increasing demand for low-latency and high-speed services in the era of 5G and beyond. However, efficient resource allocation and pricing in edge computing environments remain significant challenges. This paper adopts an economic approach to optimizing the operation and placement of edge computing systems from a macroscopic perspective, addressing a critical gap in the current literature. While previous studies have primarily focused on individual user pricing or static models, this research presents a comprehensive, dynamic pricing strategy that considers the entire market ecosystem. The study considers user demand, operating costs, and resource availability to investigate a dynamic pricing policy that enhances the efficiency and profitability of edge computing operations. The existence of equilibrium price and quantity under dynamic conditions is analyzed, and a novel pricing strategy that adapts to real-time changes in server load and market demand is proposed. The approach integrates both operational expenditures (OPEX) and capital expenditures (CAPEX) to determine optimal resource allocation, a crucial aspect often overlooked in existing research. Employing a linear demand and supply model, a case study is conducted to derive the closed-form solution for the optimal operating quantity, corresponding price, and the optimal amount of edge computing resource installation. The proposed method is then applied to a dynamic market simulation model, demonstrating its economic effectiveness. Results show significant improvements in resource utilization and profitability compared to static pricing models. This research contributes to the field by providing a robust framework for dynamic pricing in edge computing, offering valuable insights for both academic researchers and industry practitioners.