A Socially Optimal Marketplace for Splittable Task Offloading in Multi-User Multi-Server Edge Computing Networks

Mobile users can offload their tasks to adjacent edge servers to enhance service quality. These servers require suitable reimbursements to cover the operational and energy consumption costs incurred while assisting with offloaded tasks. Although previous studies have examined market mechanisms for multiple users offloading tasks to multiple servers, most of them have not investigated the market mechanism for splittable task offloading, where tasks can be divided into multiple subtasks and offloaded to multiple servers. In this work, we propose a novel edge computing marketplace that focuses on splittable task offloading in multi-user multi-server scenarios with the aim of maximizing social welfare. Designing such a marketplace presents several challenges. First, the problem of task and computing resource division introduced in this context results in a complex solution space, and the division decisions are interdependent. Second, the users and edge servers have conflicting objectives and hidden utility/cost information. To overcome these challenges and achieve socially optimal market operation, we devise an Iterative DoublE Auction (IDEA) mechanism. IDEA employs a broker to facilitate the interactions between users and edge servers and induces truthful reporting of hidden information through iterative updates to the allocation and pricing rules. Rigorous theoretical analysis and extensive simulations demonstrate the effectiveness of the proposed IDEA mechanism in achieving optimal social performance.