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LLM4ETS: Self-Evolving Algorithm Design for Edge Task Scheduling via Large Language Models
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DOI：10.1002/spe.70110.png)
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Abstract
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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.
Keywords:
edge computing
evolutionary computing
large language models
self-evolving
task scheduling
Journal
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2.7
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46
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