Two-stage computing offloading algorithm in cloud-edge collaborative scenarios based on game theory. (January 2022)

• Record Type: Journal Article
• Authors: Xu, Fei; Xie, Yue; Sun, Yongyong; Qin, Zengshi; Li, Gaojie; Zhang, Zhuoya
• Is Part Of: Computers & electrical engineering. Volume 97(2022)
• Publication Date: 2022-01
• Subjects: Cloud-edge collaboration -- Computing offloading -- DAG task model -- Energy consumption optimization -- Game theory; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107624
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Mobile Edge Computing (MEC) enables smart mobile devices to handle computationally intensive tasks with limited resources. By offloading tasks to the edge of the network closer to mobile devices, mobile devices can provide users with intelligent services with shorter latency and lower energy consumption than cloud computing. while mobile edge computing is superior in terms of reliability and low latency, it is still not a complete replacement for cloud computing. In order to make full use of the large amount of network resources of cloud computing as well as to take advantage of parallel computing, this paper investigates the partial offloading of multiple mobile devices in the cloud-side collaboration scenario. Through ​​game theory, we first obtain the edge-to-terminal optimal offloading decision, then find the cloud-edge optimal offloading decisions for all terminal devices that choose to offload to the edge, and finally reach the goal of optimal offloading on the three sides of the cloud-edge-terminal. Based on this idea, we propose a two-stage computing offloading algorithm (TSGTA). The whole algorithm is divided into two stages, and both stages are designed to offload algorithms based on game theory. The simulation results show that under the time-delay constraint, this method has an average improvement of 32% compared with the traditional method.