AoI-Aware User Service Satisfaction Enhancement in Digital Twin-Empowered Edge Computing

The emerging digital twin technique enhances the network management efficiency and provides comprehensive insights on network performance, through mapping physical objects to their digital twins. The user satisfaction on digital twin-enabled service relies on the freshness of digital twin data, whic...

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Veröffentlicht in:IEEE/ACM transactions on networking Jg. 32; H. 2; S. 1 - 14
Hauptverfasser: Li, Jing, Guo, Song, Liang, Weifa, Wang, Jianping, Chen, Quan, Xu, Zichuan, Xu, Wenzheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
age of information
Algorithms
Approximation algorithms
approximation and online algorithms
Cloud computing
Data communication
Delays
Digital twin
digital twin placement
Digital twins
Edge computing
Heuristic algorithms
Integer programming
Linear programming
Maximization
Mobile computing
mobile edge computing
Optimization
Performance evaluation
Prediction algorithms
Provisioning
Real-time systems
User satisfaction
ISSN:1063-6692, 1558-2566
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Zusammenfassung:The emerging digital twin technique enhances the network management efficiency and provides comprehensive insights on network performance, through mapping physical objects to their digital twins. The user satisfaction on digital twin-enabled service relies on the freshness of digital twin data, which is measured by the Age of Information (AoI). Due to long service delays, the use of the remote cloud for delay-sensitive service provisioning faces serious challenges. Mobile Edge Computing (MEC), as an ideal paradigm for delay-sensitive services, is able to realize real-time data communication between physical objects and their digital twins at the network edge. However, the mobility of physical objects and dynamics of user query arrivals make seamless service provisioning in MEC become challenging. In this paper, we investigate dynamic digital twin placements for improving user service satisfaction in MEC environments, by introducing a novel metric to measure user service satisfaction based on the AoI concept and formulating two user service satisfaction enhancement problems: the static and dynamic utility maximization problems under static and dynamic digital twin placement schemes. To this end, we first formulate an Integer Linear Programming (ILP) solution to the static utility maximization problem when the problem size is small; otherwise, we propose a performance-guaranteed approximation algorithm. We then propose an online algorithm with a provable competitive ratio for the dynamic utility maximization problem, by considering dynamic user query services. Finally, we evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the proposed algorithms outperform the comparison baseline algorithms, improving the algorithm performance by at least <inline-formula> <tex-math notation="LaTeX">10.7\%</tex-math> </inline-formula>, compared to the baseline algorithms.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1063-6692
1558-2566
DOI:10.1109/TNET.2023.3324704