BYOS: Knowledge-driven Large Language Models Bring Your Own Operating System More Excellent
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908828231008256 |
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| author | Lin, Hongyu Li, Yuchen Luo, Haoran Yao, Kaichun Zhang, Libo Lin, Zhenghong Xing, Mingjie Wu, Yanjun Yang, Carl |
| author_facet | Lin, Hongyu Li, Yuchen Luo, Haoran Yao, Kaichun Zhang, Libo Lin, Zhenghong Xing, Mingjie Wu, Yanjun Yang, Carl |
| contents | Operating system (OS) kernel tuning is a critical yet challenging problem for performance optimization, due to the large configuration space, complex interdependencies among configuration options, and the rapid evolution of kernel versions. Recent work has explored large language models (LLMs) for automated kernel tuning, but existing approaches often suffer from hallucinated configurations, limited interpretability, and poor robustness across workloads and kernel versions. We propose BYOS, a knowledge-driven framework that grounds LLM-based Linux kernel tuning in structured domain knowledge. BYOS incorporates three key components: (1) structured knowledge construction and mapping to bridge the semantic gap, (2) knowledge-driven configuration generation to refine the search space, and (3) continuous knowledge maintenance to adapt to kernel evolution. We evaluate BYOS on diverse workloads across multiple Linux distributions and kernel versions. Experimental results show that BYOS consistently outperforms state-of-the-art tuning baselines, achieving 7.1%-155.4% performance improvement while substantially reducing invalid configurations. These results demonstrate the effectiveness of integrating structured knowledge with LLMs for robust and scalable system optimization. The code of BYOS is available at https://github.com/LHY-24/BYOS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09663 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BYOS: Knowledge-driven Large Language Models Bring Your Own Operating System More Excellent Lin, Hongyu Li, Yuchen Luo, Haoran Yao, Kaichun Zhang, Libo Lin, Zhenghong Xing, Mingjie Wu, Yanjun Yang, Carl Operating Systems Software Engineering Operating system (OS) kernel tuning is a critical yet challenging problem for performance optimization, due to the large configuration space, complex interdependencies among configuration options, and the rapid evolution of kernel versions. Recent work has explored large language models (LLMs) for automated kernel tuning, but existing approaches often suffer from hallucinated configurations, limited interpretability, and poor robustness across workloads and kernel versions. We propose BYOS, a knowledge-driven framework that grounds LLM-based Linux kernel tuning in structured domain knowledge. BYOS incorporates three key components: (1) structured knowledge construction and mapping to bridge the semantic gap, (2) knowledge-driven configuration generation to refine the search space, and (3) continuous knowledge maintenance to adapt to kernel evolution. We evaluate BYOS on diverse workloads across multiple Linux distributions and kernel versions. Experimental results show that BYOS consistently outperforms state-of-the-art tuning baselines, achieving 7.1%-155.4% performance improvement while substantially reducing invalid configurations. These results demonstrate the effectiveness of integrating structured knowledge with LLMs for robust and scalable system optimization. The code of BYOS is available at https://github.com/LHY-24/BYOS. |
| title | BYOS: Knowledge-driven Large Language Models Bring Your Own Operating System More Excellent |
| topic | Operating Systems Software Engineering |
| url | https://arxiv.org/abs/2503.09663 |