Mobility-Aware Service Provisioning in Edge Computing via Digital Twin Replica Placements

Yuncan Zhang, Weifa Liang, Zichuan Xu, Xiaohua Jia

Abstract: Digital twin (DT) has been emerging as an enabling technology to provide seamless interactions between the virtual cyber world and the real world. The explosion of IoT devices (objects) further fuels the development of the DT technology, and paves the way to real-time monitoring, behavior simulations and decisive predictions on objects through their digital counterparts. Meanwhile, Mobile Edge Computing (MEC) has been envisioned as a promising computing paradigm for various IoT applications with stringent delay requirements. In this paper, we study mobility-aware, delay-sensitive service provisioning in a DT-empowered MEC network with the mobility of both users and objects through DT replica placements of mobile objects. To this end, we first formulate two novel optimization problems: the DT replica placement problem and the dynamic DT replica placement problem, respectively, and show NP-hardness of the two problems. We then formulate an Integer Linear Programming (ILP) solution to the DT replica placement problem when the problem size is small or medium; otherwise we devise a randomized algorithm with high probability, provided that the mobility profiles of each object and each user are given. Meanwhile, we also develop an online algorithm for the dynamic DT replica placement problem, where for a given time horizon, service requests arrive one by one without the knowledge of future arrivals, each arrived request must be responded immediately by accepting or rejecting it. However, the heterogeneity and dynamics of user requests on resource demands may lead to the removals and re-instantiations of DT instances frequently. To mitigate this, we propose an efficient prediction mechanism to reserve a certain number of DTs for future by introducing the timestamp concept. We finally evaluate the performance of the proposed algorithms by simulations. Simulation results show that the proposed algorithms are promising, and outperform the performance of other comparison counterparts.