<ul><li>Virtual Machine (VM) scheduling in cloud services is a challenging Online Dynamic Multidimensional Bin Packing (ODMBP) problem due to its complexity and fluctuating demands.</li><li>A new hierarchical language agent framework called MiCo has been proposed to address the limitations of traditional optimization methods and domain-expert-designed heuristic approaches.</li><li>MiCo utilizes a large language model (LLM)-driven heuristic design paradigm, formulating ODMBP as a Semi-Markov Decision Process with Options (SMDP-Option) for dynamic scheduling.</li><li>Experiments show that MiCo achieves a 96.9% competitive ratio in large-scale scenarios with over 10,000 virtual machines, demonstrating its effectiveness in complex and large-scale cloud environments.</li></ul>

Learning Virtual Machine Scheduling in Cloud Computing through Language Agents

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