Dynamic Migration in Digital Twin-Enabled Industrial Internet: A Stochastic Network Calculus Approach

Abstract

Digital Twin (DT) technology serves as a critical enabler in Cyber-Physical-Social Systems (CPSS), especially within Industry 5.0’s human-centric manufacturing paradigm. However, the computational intensity of processing real-time data in DT systems often leads to resource saturation and performance degradation at computation nodes. Dynamic service migration of digital twins by offloading computation-intensive tasks to resource-rich nodes to offer a promising solution, yet introduces challenges in preserving service quality during migration. Key issues include high delay, data inconsistency, service interruptions, and limited bandwidth compromising system stability. To address these challenges, this paper proposes a dynamic migration scheduling strategy for digital twins based on a Lyapunov optimization framework. Our approach integrates Stochastic Network Calculus (SNC) for Quality of Service (QoS) quantification and a persistent queue mechanism for reliability assurance. Theoretical analysis and extensive simulations demonstrate that the proposed algorithm achieves near-optimal performance with provable bounds, effectively minimizing migration-induced delay while maintaining service reliability. The results confirm that our framework consistently outperforms existing solutions in managing service migration within industrial internet systems.