Digital Twin-Enabled Service Satisfaction Enhancement in Edge Computing

The emerging digital twin technique enhances the network management efficiency and provides comprehensive insights, through mapping physical objects to their digital twins. The user satisfaction on digital twin-enabled query services relies on the freshness of digital twin data, which is measured by the Age of Information (AoI). Because the remote cloud faces challenges in providing data for users due to long service delays, Mobile Edge Computing (MEC), as a promising technology, offers real-time data communication between physical objects and their digital twins at the edge of the core network. However, the mobility of physical objects and dynamic query arrivals make efficient service provisioning in MEC become challenging. In this paper, we investigate the dynamic digital twin placement for improving user service satisfaction in MEC environments. We focus on two user service satisfaction augmentation problems under both static and dynamic digital twin placement schemes: the static and dynamic utility maximization problems. We first formulate an Integer Linear Programming (ILP) solution to the static utility maximization problem when the problem size is small; otherwise, we propose a performance- guaranteed approximation algorithm for it. We then devise an online algorithm for the dynamic utility maximization problem with a provable competitive ratio. Finally, we evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms outperform the comparison baseline algorithms, and the performance improvement is no less than 11.6%, compared with the baseline algorithms.