Tidal-Traffic-Aware Energy-Efficient Resource Matching in Edge Computing Power Networks
Ruixi Zhao, Yaru Fu, Ke Zhang, Fan Wu, Xiaoyan Huang, Yan Zhang · IEEE Internet of Things Journal · 2025
The novel notion of Edge Computing Power Networks (ECPN) has recently been proposed to provide highly flexible matching strategies among edge servers and user devices to facilitate seamless computing power and network architectures. However, recent work about ECPN is still in its infancy, and almost all work has ignored the tidal phenomenon of computing power requests from mobile user devices, including temporal characteristic of the quantity and spatial characteristic of the distribution in different periods of one day, which leads to the energy waste of idle edge servers for always keeping active. To deal with this problem, we propose the ECPN model in this article, taking into account the tidal phenomenon of mobile user devices to develop energy-efficient computing power matching strategies. Specifically, we formulate the optimization problem that encompasses computing power allocation and task matching for user devices as well as dynamic on-off for edge servers. Our main objective is to maximize the quality of service (QoS) for user devices while minimizing the energy consumption for task execution with respect to resource limitations and task requirements. To solve the formulated problem efficiently, we propose a distributed algorithm based on matching theory to determine the optimal computing power allocation, task matching and on-off strategies. Extensive numerical results show that the proposed scheme can reduce energy consumption while ensuring the QoS.