  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.
