Digital Twin-Empowered Deep Reinforcement Learning for Intelligent VNF Migration in Edge-Core Networks
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915741905715200 |
|---|---|
| author | Ahmed, Faisal Subramaniam, Suresh Matsuura, Motoharu Hasegawa, Hiroshi Lin, Shih-Chun |
| author_facet | Ahmed, Faisal Subramaniam, Suresh Matsuura, Motoharu Hasegawa, Hiroshi Lin, Shih-Chun |
| contents | The growing demand for services and the rapid deployment of virtualized network functions (VNFs) pose significant challenges for achieving low-latency and energy-efficient orchestration in modern edge-core network infrastructures. To address these challenges, this study proposes a Digital Twin (DT)-empowered Deep Reinforcement Learning framework for intelligent VNF migration that jointly minimizes average end-to-end (E2E) delay and energy consumption. By formulating the VNF migration problem as a Markov Decision Process and utilizing the Advantage Actor-Critic model, the proposed framework enables adaptive and real-time migration decisions. A key innovation of the proposed framework is the integration of a DT module composed of a multi-task Variational Autoencoder and a multi-task Long Short-Term Memory network. This combination collectively simulates environment dynamics and generates high-quality synthetic experiences, significantly enhancing training efficiency and accelerating policy convergence. Simulation results demonstrate substantial performance gains, such as significant reductions in both average E2E delay and energy consumption, thereby establishing new benchmarks for intelligent VNF migration in edge-core networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20957 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Digital Twin-Empowered Deep Reinforcement Learning for Intelligent VNF Migration in Edge-Core Networks Ahmed, Faisal Subramaniam, Suresh Matsuura, Motoharu Hasegawa, Hiroshi Lin, Shih-Chun Networking and Internet Architecture The growing demand for services and the rapid deployment of virtualized network functions (VNFs) pose significant challenges for achieving low-latency and energy-efficient orchestration in modern edge-core network infrastructures. To address these challenges, this study proposes a Digital Twin (DT)-empowered Deep Reinforcement Learning framework for intelligent VNF migration that jointly minimizes average end-to-end (E2E) delay and energy consumption. By formulating the VNF migration problem as a Markov Decision Process and utilizing the Advantage Actor-Critic model, the proposed framework enables adaptive and real-time migration decisions. A key innovation of the proposed framework is the integration of a DT module composed of a multi-task Variational Autoencoder and a multi-task Long Short-Term Memory network. This combination collectively simulates environment dynamics and generates high-quality synthetic experiences, significantly enhancing training efficiency and accelerating policy convergence. Simulation results demonstrate substantial performance gains, such as significant reductions in both average E2E delay and energy consumption, thereby establishing new benchmarks for intelligent VNF migration in edge-core networks. |
| title | Digital Twin-Empowered Deep Reinforcement Learning for Intelligent VNF Migration in Edge-Core Networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2508.20957 |