Joint Dynamic Pricing and Computing Offloading in Edge-to-Cloud Collaboration
Tong Yin, Xin Chen, Libo Jiao, Jiaxuan Liao · 2024
With the continuous development of integrated satellite ground networks, edge servers are laid out on low orbit earth (LEO) satellites to provide seamless services for some remote areas. In order to further improve service quality for mobile devices, the collaborative work between edge and cloud has received widespread attention. In this article, we consider the scenario of insufficient base station (BS) coverage in remote areas and investigate a hybrid model of computing offloading and resource allocation for edge cloud collaborative computing, in which edge servers with limited resources collaborate with the cloud by purchasing cloud computing resources. In this way, cloud server (CS) and edge servers separately price their computing and storage resources to stimulate each other to participate in cooperation and maximize their respective utility. We jointly optimize the size of offloading tasks, offloading strategies for devices and resource pricing issues for edge servers and CS based on comprehensive consideration of price cost, energy cost and latency limitation. A multi-level Stackelberg game is formulated among the CS (leader), BSs (followers) and satellites (followers) in level I-II, the BSs (leaders), satellites (leaders) and IoT devices (followers) in level II-III. Furthermore, we prove the existence and uniqueness of Stackelberg equilibrium (SE) in Stackelberg game. The SERI algorithm is proposed and simulations is conducted to show that SERI algorithm has good convergence performance and better entity utility.