Multiparticipant Double Auction for Resource Allocation and Pricing in Edge Computing

Edge computing serves as a critical solution for latency-sensitive services on mobile and IoT devices. However, the high cost and limited edge resources present significant challenges for service and infrastructure providers in establishing efficient collaborations, particularly with conflicting profit objectives. Inspired by the pseudo elbow formation of octopuses, we propose a multi-participant double auction for resource allocation and pricing between service and infrastructure providers. We introduce a neutral third-party auctioneer to eliminate direct bargaining among participants, leading to an improved amount of allocated resources and matching efficiency. The presence of heterogeneous participants, many-to-many mapping and an advisable payment strategy that satisfies economic properties exacerbate the difficulty. To address these challenges, we propose a Matching and Pricing Resource Allocation algorithm for a long-term steady market, and a Truthful Resource Allocation algorithm for a short-term market. Simulation results demonstrate that the proposed algorithms exhibit superior performance not only in maximizing social welfare and utility of both service and infrastructure providers, but also in improving resource utilization.