Dynamic task offloading for digital twin-empowered mobile edge computing via deep reinforcement learning

Abstract

Limited by battery and computing resources, the computing-intensive tasks generated by Internet of Things (IoT) devices cannot be processed all by themselves. Mobile edge computing (MEC) is a suitable solution for this problem, and the generated tasks can be offloaded from IoT devices to MEC. In this paper, we study the problem of dynamic task offloading for digital twin-empowered MEC. Digital twin techniques are applied to provide information of environment and share the training data of agent deployed on IoT devices. We formulate the task offloading problem with the goal of maximizing the energy efficiency and the workload balance among the ESs. Then, we reformulate the problem as an MDP problem and design DRL-based energy efficient task offloading (DEETO) algorithm to solve it. Comparative experiments are carried out which show the superiority of our DEETO algorithm in improving energy efficiency and balancing the workload.