Cost-Aware Digital Twin Migration in Mobile Edge Computing via Deep Reinforcement Learning

The past decade experienced an explosive growth on the number of IoT devices connected to the Internet. Digital twins (DTs) emerge as key enablers to provide digital representations of objects for their monitoring, simulation, prediction and maintenance. At the same time, mobile edge computing (MEC) is envisioned as a promising paradigm to provide various delay-sensitive services for mobile users at the edge of core networks. In this paper, we study a novel cost-aware DT migration problem for effective service provisioning in an DT-empowered MEC network within a finite time horizon, with the aim to minimize the service cost. We first show the NP-hardness of the problem, and formulate an integer linear programming solution to the offline version of the problem, assuming that the mobility information of both objects and users for the given time horizon is given. Considering the system dynamics and heterogeneity of various resource usages, the mobility of objects and users, we then develop an efficient deep reinforcement learning algorithm for the online DT migration problem. We finally evaluate the performance of the proposed algorithms. Experimental results demonstrate that the proposed algorithms are promising.