Deep Reinforcement Learning for Mobility-Aware Digital Twin Migrations in Edge Computing

The past decade witnessed an explosive growth on the number of IoT devices (objects/suppliers), including portable mobile devices, autonomous vehicles, sensors and intelligence appliances. To realize the digital representations of objects, Digital Twins (DTs) are key enablers to provide real-time monitoring, behavior simulations and predictive decisions for objects. On the other hand, Mobile Edge Computing (MEC) has been envisioned as a promising paradigm to provide delay-sensitive services for mobile users (consumers) at the network edge, e.g., real-time healthcare, AR/VR, online gaming, smart cities, and so on. In this paper, we study a novel DT migration problem for high quality service provisioning in an MEC network with the mobility of both suppliers and consumers for a finite time horizon, with the aim to minimize the sum of the accumulative DT synchronization cost of all suppliers and the total service cost of all consumers requesting for different DT services. To this end, we first show that the problem is NP-hard, and formulate an integer linear programming solution to the offline version of the problem. We then develop a Deep Reinforcement Learning (DRL) algorithm for the DT migration problem, by considering the system dynamics and heterogeneity of different resource consumptions, mobility traces of both suppliers and consumers, and workloads of cloudlets. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising.