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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.21419 |
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| _version_ | 1866908381854302208 |
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| author | Wang, Yifan Birman, Kenneth P. |
| author_facet | Wang, Yifan Birman, Kenneth P. |
| contents | Today's cloud-hosted applications and services are complex systems, and a performance or functional instability can have dozens or hundreds of potential root causes. Our hypothesis is that by combining the pattern matching capabilities of modern AI tools with a natural multi-modal RAG LLM interface, problem identification and resolution can be simplified. ARCA is a new multi-modal RAG LLM system that targets this domain. Step-wise evaluations show that ARCA outperforms state-of-the-art alternatives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21419 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diagnosing and Resolving Cloud Platform Instability with Multi-modal RAG LLMs Wang, Yifan Birman, Kenneth P. Artificial Intelligence Operating Systems Today's cloud-hosted applications and services are complex systems, and a performance or functional instability can have dozens or hundreds of potential root causes. Our hypothesis is that by combining the pattern matching capabilities of modern AI tools with a natural multi-modal RAG LLM interface, problem identification and resolution can be simplified. ARCA is a new multi-modal RAG LLM system that targets this domain. Step-wise evaluations show that ARCA outperforms state-of-the-art alternatives. |
| title | Diagnosing and Resolving Cloud Platform Instability with Multi-modal RAG LLMs |
| topic | Artificial Intelligence Operating Systems |
| url | https://arxiv.org/abs/2505.21419 |