Large Language Model-Based Task Offloading and Resource Allocation for Digital Twin Edge Computing Networks

2025/07/25 by Qiong Wu, Yu Xie, Wu, Qiong +11 · 1 citation
Computer Science · #IoT and Edge/Fog Computing #Age of Information Optimization #Mobile Crowdsensing and Crowdsourcing

paper · pdf · doi:10.48550/arxiv.2507.19050

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.

Citations

• DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV
• DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing
• DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV
• RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction
• Generative AI as a Service in 6G Edge-Cloud: Generation Task Offloading by In-context Learning
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• Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoons
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• Age-upon-Decisions Minimizing Scheduling in Internet of Things: To be Random or to be Deterministic?

Cited by

• Active Movable-Element RIS Assisted Vehicular Semantic Communications: Modeling and Optimization

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