Collaborative optimization of computational offloading and resource allocation based on Stackelberg game - Lina Li; Qinghe Yu; Chundong Wang; Jiaqi Zhao; Junan Lv; Shuxin Wang; Chuncheng Hu (PLoS ONE, 2026)
The exponential growth of the Internet of Things and mobile edge computing has intensified the need for substantial data processing and instantaneous response. Consequently, collaboration between the cloud, the edge and the end has become a key computing paradigm. However, in this architecture, task scheduling is complex, resources are heterogeneous and dynamic, and it is still a serious challenge to achieve low-latency and energy-efficient task processing. Aiming at the deficiency of dynamic collaborative optimization in the existing research, this paper introduces a collaborative optimization approach for computational offloading and resource allocation, utilizing the Stackelberg game to maximize the system’s total utility. First, an overall utility model that integrates delay, energy consumption, and revenue is constructed for application scenarios involving multi-cloud servers, multi-edge servers, and multiple users. Subsequently, a three-tier Stackelberg game model is developed in which the cloud assumes the role of the leader, focusing on the establishment of resource pricing strategies. Concurrently, the edge operates as the sub-leader, fine-tuning the distribution of computational resources in alignment with the cloud’s strategic initiatives. Meanwhile, the mobile terminal functions as the follower, meticulously optimizing the computation offloading ratio in response to the superior strategies delineated by the preceding tiers. Next, through game equilibrium analysis, the existence and uniqueness of the Stackelberg equilibrium are proven. Finally, a BI-PRO is proposed based on the backward induction resource pricing, allocation, and computation offload optimization algorithm. The experimental findings indicate that the proposed Stackelberg game method optimizes the system’s total revenue and maintains stable performance across various scenarios. These results confirm the superiority and robustness of the method.