A Diffusion-Based Framework for Configurable and Realistic Multi-Storage Trace Generation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916930685763584 |
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| author | Kim, Seohyun Lee, Junyoung Park, Jongho Koo, Jinhyung Lee, Sungjin Kim, Yeseong |
| author_facet | Kim, Seohyun Lee, Junyoung Park, Jongho Koo, Jinhyung Lee, Sungjin Kim, Yeseong |
| contents | We propose DiTTO, a novel diffusion-based framework for generating realistic, precisely configurable, and diverse multi-device storage traces. Leveraging advanced diffusion techniques, DiTTO enables the synthesis of high-fidelity continuous traces that capture temporal dynamics and inter-device dependencies with user-defined configurations. Our experimental results demonstrate that DiTTO can generate traces with high fidelity and diversity while aligning closely with guided configurations with only 8% errors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01919 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Diffusion-Based Framework for Configurable and Realistic Multi-Storage Trace Generation Kim, Seohyun Lee, Junyoung Park, Jongho Koo, Jinhyung Lee, Sungjin Kim, Yeseong Computer Vision and Pattern Recognition Performance We propose DiTTO, a novel diffusion-based framework for generating realistic, precisely configurable, and diverse multi-device storage traces. Leveraging advanced diffusion techniques, DiTTO enables the synthesis of high-fidelity continuous traces that capture temporal dynamics and inter-device dependencies with user-defined configurations. Our experimental results demonstrate that DiTTO can generate traces with high fidelity and diversity while aligning closely with guided configurations with only 8% errors. |
| title | A Diffusion-Based Framework for Configurable and Realistic Multi-Storage Trace Generation |
| topic | Computer Vision and Pattern Recognition Performance |
| url | https://arxiv.org/abs/2509.01919 |