Bridging Incentives and Dependencies: An Iterative Combinatorial Auction Approach to Dependency-Aware Offloading in Mobile Edge Computing

As mobile applications grow increasingly computation-intensive, the challenges arising from the limitations of mobile devices in terms of computing resources and battery life become more pronounced. Mobile Edge Computing (MEC) provides a promising avenue to address these challenges and enhance user experience. While existing studies have extensively explored resource allocation and task scheduling in MEC, most treat tasks as monolithic entities, overlooking the nuanced subtasks/components that often make up mobile applications. This paper endeavors to bridge the gap between the need for incentive mechanisms and the offloading of dependent computation tasks in MEC. Drawing inspiration from auction theory, we introduce a novel Multi-stage Iterative Combinatorial Double Auction (MICDA) mechanism, specifically tailored for dependent tasks in a cloud-edge-end cooperative computing scenario. Through theoretical analysis, the MICDA mechanisms demonstrate truthfulness, individual rationality, budget balance, and computational efficiency. Comprehensive experiment results further confirm its superior performance in improving application makespan and social welfare compared to other existing offloading strategies. This work validates the effective integration of dependency-aware computation offloading and auction mechanisms in overcoming economic and computational challenges in MEC systems, thereby paving the way for their potential application in broader real-world scenarios.