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

TL;DR

This paper introduces a novel LLM-based approach for task offloading and resource allocation in digital twin edge computing networks, replacing traditional MARL methods with promising results.

Contribution

It proposes a new LLM-based method for resource management in digital twin edge networks, demonstrating its effectiveness over conventional MARL techniques.

Findings

LLM-based method achieves comparable or superior performance to MARL.

The approach effectively manages task queues and resource allocation.

Experimental results validate the proposed method's efficiency.

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.