Digital Twin-Assisted URLLC-Enabled Task Offloading in Mobile Edge Network via Robust Combinatorial Optimization

Digital twin (DT)-assisted mobile edge network can achieve energy-efficient task offloading by optimizing the decision-making in real time. Although many DT-assisted task offloading solutions in mobile edge networks have been designed, stochastic asynchronizations between the DTs and physical entities are still ignored. In this paper, we investigate a task offloading problem in a DT-assisted URLLC-enabled mobile edge network which considered the uncertain deviation between DT estimated values and physical actual values. Specifically, we formulate a latency and energy consumption minimization problem by optimizing task offloading, resource allocation, and power management. To solve this problem, we propose a DT-assisted robust task offloading scheme (DTRTO) based on learning composed of decision and deviation networks. The deviation network predicts the worst-case deviations based on the pre-decision, and the decision network optimize the decision considered the worst-case deviation. The simulation results show that, compared to the baseline algorithms, the DTRTO scheme can realize low latency and energy consumption in task offloading while maintaining high robustness.