Distributed Digital Twin Migration in Multi-Tier Computing Systems

At the network edges, the multi-tier computing framework provides mobile users with efficient cloud-like computing and signal processing capabilities. Deploying digital twins in the multi-tier computing system helps to realize ultra-reliable and low-latency interactions between users and their virtual objects. Considering users in the system may roam between edge servers with limited coverage and increase the data synchronization latency to their digital twins, it is crucial to address the digital twin migration problem to enable real-time synchronization between digital twins and users. To this end, we formulate a joint digital twin migration, communication and computation resource management problem to minimize the data synchronization latency, where the time-varying network states and user mobility are considered. By decoupling edge servers under a deterministic migration strategy, we first derive the optimal communication and computation resource management policies at each server using convex optimization methods. For the digital twin migration problem between different servers, we transform it as a decentralized partially observable Markov decision process (Dec-POMDP). To solve this problem, we propose a novel agent-contribution-enabled multi-agent reinforcement learning (AC-MARL) algorithm to enable distributed digital twin migration for users, in which the counterfactual baseline method is adopted to characterize the contribution of each agent and facilitate cooperation among agents. In addition, we utilize embedding matrices to code agents' actions and states to release the scalability issue under the high dimensional state in AC-MARL. Simulation results based on two real-world taxi mobility trace datasets show that the proposed digital twin migration scheme is able to reduce 23%–30% data synchronization latency for users compared to the benchmark schemes.