An Improved and Efficient Computational Offloading Method Based on ADMM Strategy in Cloud–Edge Collaborative Computing Environment for Resilient Industry 5.0
Wanneng Shu, Shaoliang Nie, Wencheng Jian, Ge Xin
Abstract
With the rapid growth of data demand and the rise of the Industry 5.0, cloud-edge collaborative computing achieves intelligent connectivity and data sharing between devices for intelligent management and optimization of the production process. Computational offloading, as a key technology in the cloud-edge collaborative computing environment, provides higher-quality user services and realizes higher data requirements for intelligent manufacturing and production. In this paper, we proposes an Improved Efficient Alternating Direction Method of Multipliers (IEADMM) for computational offloading in cloud-edge collaborative computing environment, aiming to reduce the total system cost with the optimization goal of minimizing the total time delay. The proposed IEADMM fully utilizes the Alternating Direction Method of Multipliers ADMM) to minimize the total delay by increasing the convergence speed. The experimental result shows that our proposed IEADMM has good performance and practicality for computational offloading in cloud-edge collaborative computing environment, and can be widely applied in practical application scenarios such as intelligent monitoring, Internet of Things (IoT), mobile games, etc.