Computation Offloading and Resource Allocation for Digital Twin-empowered Mobile Edge Computing

Abstract

Mobile-edge computing (MEC) has been introduced as a promising paradigm to provide computing resources to resource-limited devices. Currently, most of the research consider static computation offloading and resource allocation in MEC, while ignoring the impact of mobility of mobile users (MUs). In this paper, we propose an algorithm to optimize the task offloading and resource allocation in Digital Twin (DT)-empowered MEC system in two steps. First, to obtain stable offloading choices, we propose an innovative algorithm for predicting stable association of MU and edge node (EN) pairs assisted by DT to enhance users’ experience. Second, taking into account the contention for resources and the constraints of offloading tasks, we construct an incentive mechanism based on reverse-auction and design an unit-revenue-based algorithm to maximize the overall revenue of MUs’ side. Simulation results demonstrate that the proposed algorithm can improve the task offloading success rate and the overall system utility, thus show the the effectiveness of our algorithm.