Multi-access edge computing (MEC) is one of the enabling technologies for high-performance computing at the edge of the 6 G networks, supporting high data rates and ultra-low service latency. Although MEC is a remedy to meet the growing demand for computation-intensive applications, the scarcity of resources at the MEC servers degrades its performance. Hence, effective resource management is essential; nevertheless, state-of-the-art research lacks efficient economic models to support the exponential growth of the MEC-enabled applications market. We focus on designing a MEC offloading service market based on a repeated auction model with multiple resource sellers (e.g., network operators and service providers) that compete to sell their computing resources to the offloading users. We design a computationally-efficient modified Generalized Second Price (GSP)-based algorithm that decides on pricing and resource allocation by considering the dynamic offloading requests arrival and the servers' computational workloads. Besides, we propose adaptive best-response bidding strategies for the resource sellers, satisfying the symmetric Nash equilibrium (SNE) and individual rationality properties. Finally, via intensive numerical results, we show the effectiveness of our proposed resource allocation mechanism.

翻译：多接入边缘计算（MEC）是实现6G网络边缘高性能计算的关键使能技术之一，能够支持高数据速率和超低服务延迟。尽管MEC是满足计算密集型应用日益增长需求的解决方案，但MEC服务器上的资源稀缺性会降低其性能。因此，有效的资源管理至关重要；然而，现有研究缺乏支持MEC应用市场指数级增长的高效经济模型。我们专注于设计一个基于重复拍卖模型的MEC卸载服务市场，其中多个资源卖方（例如网络运营商和服务提供商）竞争向卸载用户出售其计算资源。我们设计了一种计算高效的基于修正广义第二价格（GSP）的算法，该算法通过考虑动态卸载请求到达和服务器的计算负载来决定定价和资源分配。此外，我们为资源卖方提出了满足对称纳什均衡（SNE）和个体理性性质的自适应最优反应竞价策略。最后，通过大量数值结果，我们展示了所提出的资源分配机制的有效性。