LLM4ETS: Self‐Evolving Algorithm Design for Edge Task Scheduling via Large Language Models
Yuqi Zhao, Shendong Gao, Yatong Wang, Yutao Ma, Bing Li, Jian Wang, Boris Sedlak, Praveen Kumar Donta, Schahram DustdarABSTRACT
Background
With the rapid proliferation of 5G and the Internet of Things, ensuring low latency in edge computing has become crucial for real‐time processing applications. However, existing task scheduling approaches often struggle to balance multiple optimization objectives effectively due to manual parameter tuning and slow convergence to optimal performance.
Objective
To address these challenges, we propose a novel Large Language Model (LLM)‐driven edge task scheduling approach, LLM4ETS, which leverages evolutionary computation and auto‐heuristic methods to dynamically evolve towards optimal scheduling strategies.
Method
The proposed approach features three core components: (i) a micro‐parameter heuristic approach that minimizes the complexity of parameter settings, (ii) an automated heuristic design process utilizing LLMs for generating and refining scheduling strategies, and (iii) a task sequence modeling mechanism that ensures efficient and adaptive task allocation. These three components work synergistically to optimize resource utilization and reduce latency in edge computing environments.
Results
We extensively evaluate the presented approach across multiple real‐world datasets, including the Google Cluster Trace, Alibaba Cluster Trace, and EUA datasets. The experimental results demonstrate that the proposed LLM4ETS approach significantly outperforms state‐of‐the‐art methods in terms of resource utilization and task execution time, confirming its effectiveness and efficiency in edge computing environments.