Digital Twin-Empowered Network Planning for Multi-Tier Computing
Conghao Zhou, Jie Gao, Mushu Li, Xuemin (Sherman) Shen, Weihua Zhuang

TL;DR
This paper introduces a digital twin-enabled network planning framework for multi-tier 6G networks that optimizes resource reservation and reconfiguration for stateful applications, considering mobility and resource coupling.
Contribution
It proposes a novel DT-empowered approach combining multi-resource reservation, mobility-based grouping, and meta-learning for reconfiguration, advancing network resource management.
Findings
Reduces resource usage compared to benchmarks
Lowers reconfiguration costs effectively
Improves resource allocation for stateful applications
Abstract
In this paper, we design a resource management scheme to support stateful applications, which will be prevalent in 6G networks. Different from stateless applications, stateful applications require context data while executing computing tasks from user terminals (UTs). Using a multi-tier computing paradigm with servers deployed at the core network, gateways, and base stations to support stateful applications, we aim to optimize long-term resource reservation by jointly minimizing the usage of computing, storage, and communication resources and the cost from reconfiguring resource reservation. The coupling among different resources and the impact of UT mobility create challenges in resource management. To address the challenges, we develop digital twin (DT) empowered network planning with two elements, i.e., multi-resource reservation and resource reservation reconfiguration. First, DTs…
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Taxonomy
MethodsBalanced Selection
