Large Language Model-Based Task Offloading and Resource Allocation for Digital Twin Edge Computing Networks
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913959746994176 |
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| author | Wu, Qiong Xie, Yu Fan, Pingyi Qin, Dong Wang, Kezhi Cheng, Nan Letaief, Khaled B. |
| author_facet | Wu, Qiong Xie, Yu Fan, Pingyi Qin, Dong Wang, Kezhi Cheng, Nan Letaief, Khaled B. |
| contents | In this paper, we propose a general digital twin edge computing network comprising multiple vehicles and a server. Each vehicle generates multiple computing tasks within a time slot, leading to queuing challenges when offloading tasks to the server. The study investigates task offloading strategies, queue stability, and resource allocation. Lyapunov optimization is employed to transform long-term constraints into tractable short-term decisions. To solve the resulting problem, an in-context learning approach based on large language model (LLM) is adopted, replacing the conventional multi-agent reinforcement learning (MARL) framework. Experimental results demonstrate that the LLM-based method achieves comparable or even superior performance to MARL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19050 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Model-Based Task Offloading and Resource Allocation for Digital Twin Edge Computing Networks Wu, Qiong Xie, Yu Fan, Pingyi Qin, Dong Wang, Kezhi Cheng, Nan Letaief, Khaled B. Networking and Internet Architecture In this paper, we propose a general digital twin edge computing network comprising multiple vehicles and a server. Each vehicle generates multiple computing tasks within a time slot, leading to queuing challenges when offloading tasks to the server. The study investigates task offloading strategies, queue stability, and resource allocation. Lyapunov optimization is employed to transform long-term constraints into tractable short-term decisions. To solve the resulting problem, an in-context learning approach based on large language model (LLM) is adopted, replacing the conventional multi-agent reinforcement learning (MARL) framework. Experimental results demonstrate that the LLM-based method achieves comparable or even superior performance to MARL. |
| title | Large Language Model-Based Task Offloading and Resource Allocation for Digital Twin Edge Computing Networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2507.19050 |