Large Language Model-Based Task Offloading and Resource Allocation for Digital Twin Edge Computing Networks
Abstract
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.
Fields of Study & Subjects
IoT and Edge/Fog Computing
Age of Information Optimization
Mobile Crowdsensing and Crowdsourcing
Publication details
- Journal / source
- IEEE Transactions on Mobile Computing
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 25
- Issue
- 7
- Pages
- 10290–10301
- Publication date
- 16 February 2026
- Language
- EN