An online auction-based incentive mechanism for soft-deadline tasks in Collaborative Edge Computing

Recently, Collaborative Edge Computing (CEC) has been proposed as an effective method to improve the performance of Mobile Edge Computing (MEC) systems by offloading workload from busy MEC servers to idle ones. Although extensive research has been conducted to study the system design and scheduling algorithms in CEC, the incentive mechanism between end-users and collaborative servers yet receives much less attention. To fill the gap, in this paper, we propose an online auction-based incentive mechanism where the task requests arrive stochastically and MEC servers need to decide whether to accept them without future information. To guarantee the applicability of our mechanism, we consider a comprehensive model where both the valuation of tasks and operation costs of edge servers are represented by general functions. Through theoretical analysis, we formally proved our mechanism achieves desirable properties, such as truthfulness and polynomial time complexity. Based on the primal-dual optimization framework, we further demonstrate the competitive ratio of our mechanism with respect to the optimal offline social welfare. Extensive simulations are conducted to verify our theoretical analysis and validate the effectiveness of our mechanism.(c) 2022 Published by Elsevier B.V.