计算机科学
服务(商务)
服务质量
对象(语法)
移动边缘计算
边缘设备
推论
GSM演进的增强数据速率
分布式计算
边缘计算
计算机网络
云计算
服务器
电信
人工智能
操作系统
经济
经济
作者
Xianwen Liang,Weifa Liang,Zichuan Xu,Yuncan Zhang,Xiaohua Jia
出处
期刊:IEEE Transactions on Services Computing
[Institute of Electrical and Electronics Engineers]
日期:2024-01-01
卷期号:: 1-15
被引量:1
标识
DOI:10.1109/tsc.2023.3341988
摘要
Mobile Edge Computing (MEC) has emerged as a promising platform to provide various services for mobile applications at the edge of core networks while meeting stringent service delay requirements of users. Digital twin (DT) that is a mirror of a physical object in cyberspace now becomes a key player in smart cities and the Metaverse, which can be used to simulate or predict the behaviours of the object in future. To enable such a simulation or predication to be more accurate and robust, the state of the digital twin needs to be synchronized (updated) with its object quite often. The quality of inference services in a DT-empowered MEC network usually is determined by the state freshness of service models, while a service model further is determined by the state freshness of its source DT data. It is vital to refresh the states of service models frequently in order to provide high quality inference services. In this paper, we study how to maximize the state freshness of both digital twins and a set of inference service models that are built upon digital twins in an MEC network, while the state freshness of a DT or a service model is achieved through frequent synchronizations between the DT and its physical object. Specifically, we first study a novel cost-aware average model freshness maximization problem with the aim to maximize the average freshness of the states of inference service models while minimizing the cost of achieving the model freshness, and show the NP-hardness of the problem. We then formulate an integer linear programming solution for the offline version of the problem, and devise a performance-guaranteed approximation algorithm for a special case of problem when the monitoring period consists of a single time slot only. Also, we develop an efficient online algorithm for the problem through scheduling objects to upload their update data to their digital twins in the network at each time slot efficiently. We finally evaluate the performance of the proposed algorithms through simulations. Simulation results demonstrate that the proposed algorithms are promising.
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