{"ID":2875248,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.01919","arxiv_id":"2509.01919","title":"A Diffusion-Based Framework for Configurable and Realistic Multi-Storage Trace Generation","abstract":"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.","short_abstract":"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-d...","url_abs":"https://arxiv.org/abs/2509.01919","url_pdf":"https://arxiv.org/pdf/2509.01919v1","authors":"[\"Seohyun Kim\",\"Junyoung Lee\",\"Jongho Park\",\"Jinhyung Koo\",\"Sungjin Lee\",\"Yeseong Kim\"]","published":"2025-09-02T03:29:48Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.PF\"]","methods":"[\"Diffusion Model\"]","has_code":false}