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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2411.05349 |
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| _version_ | 1866909380475092992 |
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| author | Shi, Honghao Cheng, Longkai Wu, Wenli Wang, Yuhang Liu, Xuan Nie, Shaokai Wang, Weixv Min, Xuebin Men, Chunlei Lin, Yonghua |
| author_facet | Shi, Honghao Cheng, Longkai Wu, Wenli Wang, Yuhang Liu, Xuan Nie, Shaokai Wang, Weixv Min, Xuebin Men, Chunlei Lin, Yonghua |
| contents | Recent advancements in Large Language Models (LLMs) and related technologies such as Retrieval-Augmented Generation (RAG) and Diagram of Thought (DoT) have enabled the creation of autonomous intelligent systems capable of performing cluster diagnostics and troubleshooting. By integrating these technologies with self-play methodologies, we have developed an LLM-agent system designed to autonomously diagnose and resolve issues within AI clusters. Our innovations include a knowledge base tailored for cluster diagnostics, enhanced LLM algorithms, practical deployment strategies for agents, and a benchmark specifically designed for evaluating LLM capabilities in this domain. Through extensive experimentation across multiple dimensions, we have demonstrated the superiority of our system in addressing the challenges faced in cluster diagnostics, particularly in detecting and rectifying performance issues more efficiently and accurately than traditional methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05349 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Cluster Resilience: LLM-agent Based Autonomous Intelligent Cluster Diagnosis System and Evaluation Framework Shi, Honghao Cheng, Longkai Wu, Wenli Wang, Yuhang Liu, Xuan Nie, Shaokai Wang, Weixv Min, Xuebin Men, Chunlei Lin, Yonghua Artificial Intelligence Distributed, Parallel, and Cluster Computing 68T42 Recent advancements in Large Language Models (LLMs) and related technologies such as Retrieval-Augmented Generation (RAG) and Diagram of Thought (DoT) have enabled the creation of autonomous intelligent systems capable of performing cluster diagnostics and troubleshooting. By integrating these technologies with self-play methodologies, we have developed an LLM-agent system designed to autonomously diagnose and resolve issues within AI clusters. Our innovations include a knowledge base tailored for cluster diagnostics, enhanced LLM algorithms, practical deployment strategies for agents, and a benchmark specifically designed for evaluating LLM capabilities in this domain. Through extensive experimentation across multiple dimensions, we have demonstrated the superiority of our system in addressing the challenges faced in cluster diagnostics, particularly in detecting and rectifying performance issues more efficiently and accurately than traditional methods. |
| title | Enhancing Cluster Resilience: LLM-agent Based Autonomous Intelligent Cluster Diagnosis System and Evaluation Framework |
| topic | Artificial Intelligence Distributed, Parallel, and Cluster Computing 68T42 |
| url | https://arxiv.org/abs/2411.05349 |