Priority-Based Offloading Optimization in Cloud-Edge Collaborative Computing

As an emerging computing paradigm, cloud-edge collaborative computing (CECC) combines computing resources at the back-end and the edge of the network to provide more flexible service delivery, thus striking a good balance between abundant computing resources and high responsiveness. However, mobile devices (MDs) must make strategic offloading decisions in such an environment. Although existing research has made remarkable progress in computation offloading strategies, most works ignore multi-priority settings in complex application scenarios. In this article, we focus on the impact of multi-priority settings and mixed queue disciplines on offloading decisions in CECC. First, we utilize queueing models to characterize all computing nodes in the environment and establish mathematical models to describe the considered scenario. Second, we formulate offloading decisions of the target MD into three multi-variable optimization problems to investigate the cost-performance tradeoff. Third, we propose numerical algorithms based on the Karush-Kuhn-Tucke conditions to address these problems. Finally, we construct numerical examples, a comparative experiment, and a simulation experiment to demonstrate the effectiveness of our methods. Our work provides important insights into the optimization of computation offloading for MDs in complex application scenarios, which can help achieve a better cost-performance tradeoff in CECC.