# Learning Virtual Machine Scheduling in Cloud Computing through Language Agents

Source: [Learning Virtual Machine Scheduling in Cloud Computing through Language Agents](<https://www.sota2.com/research/paper/68fa78961e209131cea79265>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-07T05:00:04.447Z

Authors: JieHao Wu, Ziwei Wang, Junjie Sheng, Wenhao Li, Xiangfeng Wang, Jun Luo

Published: 2025-05-15T09:42:11.000Z

## Abstract

In cloud services, virtual machine (VM) scheduling is a typical Online Dynamic Multidimensional Bin Packing (ODMBP) problem, characterized by large-scale complexity and fluctuating demands. Traditional optimization methods struggle to adapt to real-time changes, domain-expert-designed heuristic approaches suffer from rigid strategies, and existing learning-based methods often lack generalizability and interpretability. To address these limitations, this paper proposes a hierarchical language agent framework named MiCo, which provides a large language model (LLM)-driven heuristic design paradigm for solving ODMBP. Specifically, ODMBP is formulated as a Semi-Markov Decision Process with Options (SMDP-Option), enabling dynamic scheduling through a two-stage architecture, i.e., Option Miner and Option Composer. Option Miner utilizes LLMs to discover diverse and useful non-context-aware strategies by interacting with constructed environments. Option Composer employs LLMs to discover a composing strategy that integrates the non-context-aware strategies with the contextual ones. Extensive experiments on real-world enterprise datasets demonstrate that MiCo achieves a 96.9\\% competitive ratio in large-scale scenarios involving more than 10,000 virtual machines. It maintains high performance even under nonstationary request flows and diverse configurations, thus validating its effectiveness in complex and large-scale cloud environments.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [VM Scheduling on Azure (train)](<https://www.sota2.com/research/sota/vm-scheduling-on-azure-train.md>): VM Scheduling; Azure (train); Mean Score: 71.5.

- [VM Scheduling on Huawei-East-1 (train)](<https://www.sota2.com/research/sota/vm-scheduling-on-huawei-east-1-train.md>): VM Scheduling; Huawei-East-1 (train); Performance Ratio (S1): 99.9.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2505.10117>)

[PDF](<https://arxiv.org/pdf/2505.10117.pdf>)