# Digital Twin-Empowered Network Planning for Multi-Tier Computing

arXiv: https://arxiv.org/abs/2210.02616 · HTML: https://mlsanity.com/paper/2210.02616/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [The Role of Caching in Future Communication Systems and Networks](https://mlsanity.com/paper/1805.11721/) (2018)
- [Decentralized Computation Offloading Game For Mobile Cloud Computing](https://mlsanity.com/paper/1404.3200/) (2014)
- [Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing](https://mlsanity.com/paper/1809.05239/) (2018)
- [Faasm: Lightweight Isolation for Efficient Stateful Serverless Computing](https://mlsanity.com/paper/2002.09344/) (2020)
- [Service Placement with Provable Guarantees in Heterogeneous Edge Computing Systems](https://mlsanity.com/paper/1906.07055/) (2019)
- [Budget-constrained Edge Service Provisioning with Demand Estimation via Bandit Learning](https://mlsanity.com/paper/1903.09080/) (2019)
- [Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in Space-Air-Ground Integrated Network](https://mlsanity.com/paper/2010.01471/) (2020)
- [Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks](https://mlsanity.com/paper/1703.06058/) (2017)
- [Rate-Utility Optimized Streaming of Volumetric Media for Augmented Reality](https://mlsanity.com/paper/1804.09864/) (2018)
- [Online Service Provisioning in NFV-enabled Networks Using Deep Reinforcement Learning](https://mlsanity.com/paper/2111.02209/) (2021)

## Built on by (most cited first, 4 of 4)

- [A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems](https://mlsanity.com/paper/2212.01351/) (2022)
- [Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review](https://mlsanity.com/paper/2501.01007/) (2025)
- [Optimizing Wireless Resource Management and Synchronization in Digital Twin Networks](https://mlsanity.com/paper/2502.05116/) (2025)
- [Digital Twin-Based User-Centric Edge Continual Learning in Integrated Sensing and Communication](https://mlsanity.com/paper/2311.12223/) (2023)

## Abstract (arXiv)

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 are designed for collecting UT status data, based on which UTs are grouped according to their mobility patterns. Second, an algorithm is proposed to customize resource reservation for different groups to satisfy their different resource demands. Last, a Meta-learning-based approach is developed to reconfigure resource reservation for balancing the network resource usage and the reconfiguration cost. Simulation results demonstrate that the proposed DT-empowered network planning outperforms benchmark frameworks by using less resources and incurring lower reconfiguration costs.